On this first assignment, applying the basic functions of the Igraph package is required. The following datasets are going to be used:
You have to complete the code chunks in this document but also analyze the results, extract insights and answer the short questions. Fill the CSV attached with your answers, sometimes just the number is enough, some others just a small sentence or paragraph. Remember to change the header with your email.
In your submission please upload both this document in HTML and the CSV with the solutions.
!pip install jupyter_contrib_nbextensions
!pip install python-igraph
!apt-get install libcairo2-dev libjpeg-dev libgif-dev
!pip install pycairo
!pip install cairocffi
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Done Building dependency tree Reading state information... Done libcairo2-dev is already the newest version (1.16.0-4ubuntu1). libgif-dev is already the newest version (5.1.9-1). libjpeg-dev is already the newest version (8c-2ubuntu8). 0 upgraded, 0 newly installed, 0 to remove and 24 not upgraded. Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/ Requirement already satisfied: pycairo in /usr/local/lib/python3.9/dist-packages (1.23.0) Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/ Requirement already satisfied: cairocffi in /usr/local/lib/python3.9/dist-packages (1.5.0) Requirement already satisfied: cffi>=1.1.0 in /usr/local/lib/python3.9/dist-packages (from cairocffi) (1.15.1) Requirement already satisfied: pycparser in /usr/local/lib/python3.9/dist-packages (from cffi>=1.1.0->cairocffi) (2.21)
import networkx as nx
import pandas as pd
import matplotlib.pyplot as plt
%matplotlib inline
import io
import igraph as ig
from igraph import Graph, EdgeSeq
import numpy as np
import plotly.graph_objects as go
import plotly.graph_objs as go
import plotly.io as pio
import plotly.express as px
import plotly.subplots as sp
import cairo
import cairocffi as cairo
import statistics
In this section, the goal is loading the datasets given, building the graph and analyzing basics metrics. Include the edge or node attributes you consider.Describe the values provided by summary function on the graph object.
from google.colab import drive
drive.mount('/content/drive')
Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount("/content/drive", force_remount=True).
# Read files
#file = '/content/drive/MyDrive/imdb_actors_key_sol (1).csv'
file ='/content/drive/MyDrive/imdb_actors_key_sol (1).csv'
nodes = pd.read_csv(file,sep=';')
nodes.head()
| id | ActorName | movies_95_04 | main_genre | genres | |
|---|---|---|---|---|---|
| 0 | n15629 | Rudder, Michael (I) | 12 | Thriller | Action:1,Comedy:1,Drama:1,Fantasy:1,Horror:1,N... |
| 1 | n5026 | Morgan, Debbi | 16 | Drama | Comedy:2,Documentary:1,Drama:6,Horror:2,NULL:3... |
| 2 | n11252 | Bellows, Gil | 33 | Drama | Comedy:6,Documentary:1,Drama:7,Family:1,Fantas... |
| 3 | n5150 | Dray, Albert | 20 | Comedy | Comedy:6,Crime:1,Documentary:1,Drama:4,NULL:5,... |
| 4 | n4057 | Daly, Shane (I) | 18 | Drama | Comedy:2,Crime:1,Drama:7,Horror:1,Music:1,Musi... |
# Locally Read the csv file into dataframe
#3df = pd.read_csv('/Users/stephaniegessler/Downloads/imdb_actors_key_sol (1).csv',sep=';')
#df.head()
# locally Read the csv file into dataframe
#df_key= pd.read_csv('/Users/stephaniegessler/Downloads/imdb_actor_edges_sol (1).csv',sep=';')
#df_key.head()
# Read files
#file = 'drive/MyDrive/imdb_actor_edges_sol (1).csv'
file = '/content/drive/MyDrive/imdb_actor_edges_sol (1).csv'
edges = pd.read_csv(file,sep=';')
edges = edges.rename(columns={'from': 'source', 'to': 'target'})
edges.head()
| source | target | weight | |
|---|---|---|---|
| 0 | n17776 | n17778 | 6 |
| 1 | n5578 | n9770 | 3 |
| 2 | n5578 | n929 | 2 |
| 3 | n5578 | n9982 | 2 |
| 4 | n1835 | n6278 | 2 |
In graph theory, the weight of an edge is typically used to represent the cost or distance between two nodes. When computing the shortest path between two nodes in a graph, the algorithm tries to find the path with the minimum weight.
However, in some cases, it may be more intuitive to think of a larger weight as indicating a stronger connection between two nodes. In these cases, taking the inverse of the weight 1/weight can be useful. This way, larger weights result in smaller values, and vice versa.
If we are modeling the social network of the actors where the weight of an edge represents the strength of a relationship between two people, a larger weight could indicate a stronger relationship since they worked together in more movies together. In this case, taking the inverse of the weight would mean that larger weights, the stronger relationship would result in shorter distances in the graph, which would align more with our intuition.
# create a new dataframe for the edges
edges_inverse = edges[['source', 'target', 'weight']].copy()
# calculate the inverse of the weight column
edges_inverse ['weight'] = edges['weight'].apply(lambda x: 1/x)
edges_inverse.head()
| source | target | weight | |
|---|---|---|---|
| 0 | n17776 | n17778 | 0.166667 |
| 1 | n5578 | n9770 | 0.333333 |
| 2 | n5578 | n929 | 0.500000 |
| 3 | n5578 | n9982 | 0.500000 |
| 4 | n1835 | n6278 | 0.500000 |
#statistics of df
nodes.describe()
| movies_95_04 | |
|---|---|
| count | 17577.000000 |
| mean | 20.545258 |
| std | 20.686926 |
| min | 10.000000 |
| 25% | 12.000000 |
| 50% | 15.000000 |
| 75% | 22.000000 |
| max | 540.000000 |
There are 17577 nodes in the graph but we will see this later. On average, each node is in 20.54 movies from the period 1995-2004. The standard deviation of the number of movies each node is in is 20.69, indicating a relatively wide range of values as the minimum number of movies a node is in is 10, while the maximum is 540.
value_counts =nodes['main_genre'].value_counts()
print(value_counts)
Drama 7302 Comedy 2928 Adult 1811 Romance 1027 Thriller 947 Crime 711 Music 631 Action 537 Sci-Fi 421 Family 359 Animation 284 Horror 171 Fantasy 137 War 69 Mystery 66 Adventure 63 Western 24 Musical 21 Name: main_genre, dtype: int64
The top three movies in the main genre are Drama, Comedy and Adult for most actors.
17577 actors are in the dataset, which can be validated counting the Id numbers
value_counts = nodes.groupby(['id']).size().count()
value_counts
17577
For further anylyis the genres are Split into seperate columns, we keep also NULL as there are high number of actors which indenticates in genre NULL.
# Split the 'genres' column by ',' to create separate columns for each genre
genres_df = nodes['genres'].str.split(',', expand=True)
# Extracts the unique genre keys and splits the genre count by ":". Flatten helps to make a single array and set makes it unique
genres_keys = set(genres_df.apply(lambda x: x.str.split(':', expand=True)[0]).values.flatten())
#Remove None and Null
genres_keys.remove(None)
#genres_keys.remove('NULL')
print(genres_keys)
df_with_genres = nodes.copy().drop('genres', axis=1)
# The function extracts the count of a specific genre in a string of genres and their respective counts
def get_genre_count(x, genre):
genre_counts = x.split(',')
for genre_count in genre_counts:
if genre_count.split(':')[0] == genre:
return genre_count.split(':')[1]
return 0
for genre in genres_keys:
df_with_genres[genre] = nodes['genres'].apply(lambda x: get_genre_count(x, genre))
df_with_genres.head()
{'Music', 'Animation', 'Action', 'Crime', 'Horror', 'Comedy', 'Adult', 'Documentary', 'Drama', 'Romance', 'Sci-Fi', 'Thriller', 'Western', 'Mystery', 'NULL', 'Family', 'Short', 'Fantasy', 'Adventure', 'War', 'Musical'}
| id | ActorName | movies_95_04 | main_genre | Music | Animation | Action | Crime | Horror | Comedy | Adult | Documentary | Drama | Romance | Sci-Fi | Thriller | Western | Mystery | NULL | Family | Short | Fantasy | Adventure | War | Musical | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | n15629 | Rudder, Michael (I) | 12 | Thriller | 0 | 0 | 1 | 0 | 1 | 1 | 0 | 0 | 1 | 1 | 1 | 2 | 0 | 0 | 2 | 0 | 0 | 1 | 0 | 1 | 0 |
| 1 | n5026 | Morgan, Debbi | 16 | Drama | 0 | 0 | 0 | 0 | 2 | 2 | 0 | 1 | 6 | 2 | 0 | 0 | 0 | 0 | 3 | 0 | 0 | 0 | 0 | 0 | 0 |
| 2 | n11252 | Bellows, Gil | 33 | Drama | 0 | 0 | 0 | 0 | 1 | 6 | 0 | 1 | 7 | 6 | 0 | 4 | 0 | 2 | 2 | 1 | 2 | 1 | 0 | 0 | 0 |
| 3 | n5150 | Dray, Albert | 20 | Comedy | 0 | 0 | 0 | 1 | 0 | 6 | 0 | 1 | 4 | 1 | 0 | 1 | 0 | 0 | 5 | 0 | 1 | 0 | 0 | 0 | 0 |
| 4 | n4057 | Daly, Shane (I) | 18 | Drama | 1 | 0 | 0 | 1 | 1 | 2 | 0 | 0 | 7 | 1 | 0 | 4 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
Min weight is 2 and the maxium is 118 weight, but the mean is 3.18 as weight for the edges.
edges.describe()
| weight | |
|---|---|
| count | 287074.000000 |
| mean | 3.281206 |
| std | 2.991388 |
| min | 2.000000 |
| 25% | 2.000000 |
| 50% | 2.000000 |
| 75% | 3.000000 |
| max | 118.000000 |
Based on these results, we can conclude that the majority of edges have a weight of 2 or 3, with 50% quantiles of the edges having a weight of 2. There are some edges with a high weight (up to 118), but these are likely to be outliers. Overall, the weights of the edges in the graph seem to be relatively low, indicating that there are not many strong connections between the nodes.
value_counts = edges.groupby(['source', 'target']).size().count()
print(value_counts)
287074
g = ig.Graph()
g = Graph.DataFrame(edges=edges, vertices=df_with_genres, directed=False, use_vids=False)
g_inv = ig.Graph()
g_inv = Graph.DataFrame(edges=edges_inverse, vertices=df_with_genres, directed=False, use_vids=False)
print(g.summary())
IGRAPH UNW- 17577 287074 -- + attr: Action (v), ActorName (v), Adult (v), Adventure (v), Animation (v), Comedy (v), Crime (v), Documentary (v), Drama (v), Family (v), Fantasy (v), Horror (v), Music (v), Musical (v), Mystery (v), NULL (v), Romance (v), Sci-Fi (v), Short (v), Thriller (v), War (v), Western (v), main_genre (v), movies_95_04 (v), name (v), weight (e)
ig.plot(g, bbox=(1000, 600),vertex_color='green', target='myplot.png')
In the graph you see that not all nodes are connected (disconnected graph). In a disconnected graph, there are one or more sets of nodes that have no edges connecting them to any other nodes in the graph. This creates isolated clusters or components of nodes.
Two groups of actors who have no actors link in common, they would form two disconnected components in the graph.
# create an empty NetworkX graph
G = nx.Graph()
# add nodes to the graph
G.add_nodes_from(nodes['id'])
# add edges to the graph with weights
for index, row in edges.iterrows():
G.add_edge(row['source'], row['target'], weight=row['weight'])
from pyvis.network import Network
# create a PyVis network object
net = Network(height='750px', width='100%', bgcolor='#222222', font_color='white')
net.from_nx(G)
net.show('mygraph.html')
# Iterate over the rows of the nodes dataframe
for i, row in nodes.iterrows():
# Add the node to the graph with attributes
G.add_node(row['id'], ActorName=row['ActorName'], movies_95_04=row['movies_95_04'], main_genre=row['main_genre'], genres=row['genres'])
The connected components gives you the number of subgraphs, where there is a path between any two nodes. Since we have 19 subgraphs, all nodes in a connected component can be reached from any other node in the same component. If a graph has only one connected component, then it is a connected graph. If a graph has more than one connected component, then it is a disconnected graph.
nx.number_connected_components(G)
19
# Get the connected components
components = g.clusters()
# Print the size and membership of each component
for i, c in enumerate(components):
print(f"Component {i} has size {len(c)} and contains vertices {c}")
Component 0 has size 17455 and contains vertices [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182, 183, 184, 185, 186, 187, 188, 189, 190, 191, 192, 193, 194, 195, 196, 197, 198, 199, 200, 201, 202, 203, 204, 205, 206, 207, 208, 209, 210, 211, 212, 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17089, 17090, 17091, 17092, 17093, 17094, 17095, 17096, 17097, 17098, 17099, 17100, 17101, 17102, 17104, 17105, 17106, 17107, 17108, 17109, 17110, 17111, 17112, 17113, 17114, 17115, 17116, 17117, 17118, 17119, 17120, 17121, 17122, 17123, 17124, 17125, 17126, 17128, 17129, 17130, 17131, 17132, 17133, 17134, 17135, 17136, 17137, 17138, 17139, 17140, 17141, 17142, 17143, 17144, 17145, 17146, 17147, 17148, 17149, 17150, 17151, 17152, 17153, 17154, 17155, 17156, 17157, 17158, 17159, 17160, 17161, 17162, 17163, 17164, 17165, 17166, 17167, 17168, 17169, 17170, 17171, 17172, 17173, 17174, 17175, 17176, 17177, 17178, 17179, 17180, 17181, 17182, 17183, 17184, 17185, 17186, 17187, 17188, 17189, 17190, 17191, 17192, 17193, 17194, 17195, 17196, 17197, 17198, 17199, 17200, 17201, 17202, 17203, 17204, 17205, 17206, 17207, 17208, 17209, 17210, 17211, 17212, 17213, 17214, 17215, 17216, 17217, 17218, 17219, 17220, 17221, 17222, 17223, 17224, 17225, 17226, 17227, 17228, 17229, 17230, 17231, 17232, 17233, 17234, 17235, 17236, 17237, 17238, 17239, 17240, 17241, 17242, 17243, 17244, 17245, 17246, 17247, 17248, 17249, 17250, 17251, 17252, 17253, 17254, 17255, 17256, 17257, 17258, 17259, 17260, 17261, 17262, 17263, 17264, 17265, 17266, 17267, 17268, 17269, 17270, 17271, 17272, 17273, 17274, 17275, 17276, 17277, 17278, 17279, 17280, 17281, 17282, 17283, 17284, 17285, 17286, 17287, 17288, 17289, 17290, 17291, 17292, 17293, 17294, 17295, 17296, 17297, 17298, 17299, 17300, 17301, 17302, 17303, 17304, 17305, 17306, 17307, 17308, 17309, 17310, 17311, 17312, 17313, 17314, 17315, 17316, 17317, 17318, 17319, 17320, 17321, 17322, 17323, 17324, 17325, 17326, 17327, 17328, 17329, 17330, 17331, 17332, 17333, 17334, 17335, 17336, 17337, 17338, 17339, 17340, 17341, 17342, 17343, 17344, 17345, 17346, 17347, 17348, 17349, 17350, 17351, 17352, 17353, 17354, 17355, 17357, 17358, 17359, 17360, 17361, 17362, 17363, 17364, 17365, 17366, 17367, 17368, 17369, 17370, 17371, 17372, 17373, 17374, 17375, 17376, 17377, 17378, 17379, 17380, 17381, 17382, 17383, 17384, 17385, 17386, 17387, 17388, 17389, 17390, 17391, 17392, 17393, 17394, 17395, 17396, 17397, 17398, 17399, 17400, 17401, 17402, 17403, 17404, 17405, 17406, 17407, 17408, 17409, 17410, 17411, 17412, 17413, 17414, 17415, 17416, 17417, 17418, 17419, 17420, 17421, 17422, 17423, 17424, 17425, 17426, 17427, 17428, 17429, 17430, 17431, 17433, 17434, 17435, 17436, 17437, 17438, 17439, 17440, 17441, 17442, 17443, 17444, 17445, 17446, 17447, 17448, 17449, 17450, 17451, 17452, 17453, 17454, 17455, 17456, 17457, 17458, 17459, 17460, 17461, 17462, 17463, 17464, 17465, 17466, 17468, 17469, 17470, 17471, 17472, 17473, 17474, 17475, 17476, 17477, 17478, 17479, 17480, 17481, 17482, 17483, 17484, 17485, 17486, 17487, 17488, 17489, 17490, 17491, 17492, 17493, 17494, 17495, 17496, 17497, 17498, 17499, 17500, 17501, 17502, 17503, 17504, 17505, 17506, 17507, 17508, 17509, 17510, 17511, 17512, 17513, 17514, 17515, 17516, 17517, 17518, 17519, 17520, 17521, 17522, 17523, 17524, 17525, 17526, 17527, 17528, 17529, 17530, 17531, 17532, 17533, 17534, 17535, 17536, 17537, 17538, 17539, 17540, 17541, 17542, 17544, 17545, 17546, 17547, 17548, 17549, 17550, 17551, 17552, 17553, 17554, 17555, 17556, 17557, 17558, 17559, 17560, 17561, 17562, 17563, 17564, 17565, 17566, 17567, 17568, 17569, 17570, 17571, 17572, 17573, 17574, 17575, 17576] Component 1 has size 22 and contains vertices [71, 85, 756, 873, 896, 959, 1493, 2631, 3075, 3269, 6282, 6729, 7036, 7044, 7657, 12094, 13262, 13312, 14138, 14444, 15967, 16031] Component 2 has size 23 and contains vertices [459, 2176, 4071, 5277, 5552, 5842, 6428, 7907, 8904, 9428, 9789, 9813, 10451, 10989, 11593, 13335, 13360, 14130, 15054, 15177, 16883, 17127, 17543] Component 3 has size 4 and contains vertices [550, 2815, 15166, 15449] Component 4 has size 33 and contains vertices [607, 1870, 2596, 2632, 4850, 5682, 6735, 6818, 6992, 8052, 8084, 8210, 9117, 9979, 10948, 10983, 11020, 11176, 11851, 12004, 12716, 12734, 13241, 13388, 14466, 14622, 14876, 15145, 15892, 16044, 16339, 17085, 17356] Component 5 has size 2 and contains vertices [1495, 11234] Component 6 has size 3 and contains vertices [1814, 14828, 17467] Component 7 has size 11 and contains vertices [2109, 2320, 2567, 5377, 6659, 7288, 9514, 12756, 13875, 16262, 17103] Component 8 has size 2 and contains vertices [2461, 2480] Component 9 has size 3 and contains vertices [2586, 4062, 12763] Component 10 has size 2 and contains vertices [2838, 3815] Component 11 has size 2 and contains vertices [3910, 12772] Component 12 has size 2 and contains vertices [3997, 7258] Component 13 has size 2 and contains vertices [6281, 15732] Component 14 has size 3 and contains vertices [6367, 8211, 13001] Component 15 has size 2 and contains vertices [6710, 11439] Component 16 has size 2 and contains vertices [9693, 12981] Component 17 has size 2 and contains vertices [12747, 16050] Component 18 has size 2 and contains vertices [14206, 17432]
<ipython-input-33-b0350cd9ff9f>:2: DeprecationWarning: Graph.clusters() is deprecated; use Graph.connected_components() instead
Overall the entire graph consists of 17577 nodes and 28704 edges, where subgraph 1 has 99% of the nodes and edges.
# Compute some basic metrics of the entire graph
print(g.summary())
print("Number of nodes:", len(g.vs))
print("Number of edges:", len(g.es))
IGRAPH UNW- 17577 287074 -- + attr: Action (v), ActorName (v), Adult (v), Adventure (v), Animation (v), Comedy (v), Crime (v), Documentary (v), Drama (v), Family (v), Fantasy (v), Horror (v), Music (v), Musical (v), Mystery (v), NULL (v), Romance (v), Sci-Fi (v), Short (v), Thriller (v), War (v), Western (v), main_genre (v), movies_95_04 (v), name (v), weight (e) Number of nodes: 17577 Number of edges: 287074
#Now looking at the subgraphs create the list of subgraphs
subgraphs = g.decompose()
# create the list of subgraphs for the inverse
subgraphs_inv = g_inv.decompose()
for i, sg in enumerate(subgraphs):
print(f"Subgraph {i+1}:")
print(f"Number of nodes: {sg.vcount()}")
print(f"Number of edges: {sg.ecount()}")
Subgraph 1: Number of nodes: 17455 Number of edges: 286911 Subgraph 2: Number of nodes: 22 Number of edges: 30 Subgraph 3: Number of nodes: 23 Number of edges: 28 Subgraph 4: Number of nodes: 4 Number of edges: 6 Subgraph 5: Number of nodes: 33 Number of edges: 55 Subgraph 6: Number of nodes: 2 Number of edges: 1 Subgraph 7: Number of nodes: 3 Number of edges: 2 Subgraph 8: Number of nodes: 11 Number of edges: 27 Subgraph 9: Number of nodes: 2 Number of edges: 1 Subgraph 10: Number of nodes: 3 Number of edges: 3 Subgraph 11: Number of nodes: 2 Number of edges: 1 Subgraph 12: Number of nodes: 2 Number of edges: 1 Subgraph 13: Number of nodes: 2 Number of edges: 1 Subgraph 14: Number of nodes: 2 Number of edges: 1 Subgraph 15: Number of nodes: 3 Number of edges: 2 Subgraph 16: Number of nodes: 2 Number of edges: 1 Subgraph 17: Number of nodes: 2 Number of edges: 1 Subgraph 18: Number of nodes: 2 Number of edges: 1 Subgraph 19: Number of nodes: 2 Number of edges: 1
Subgraph 1 has the most edges and nodes,the remaining have few or just single edges.
#identify the biggest subgraph
largest_subgraph = max(subgraphs, key=lambda subgraph: len(subgraph.vs))
print("Largest subgraph index:", subgraphs.index(largest_subgraph))
print("Number of vertices in largest subgraph:", len(largest_subgraph.vs))
Largest subgraph index: 0 Number of vertices in largest subgraph: 17455
# Iterate over subgraphs and calculate their average degree
for i, subgraph in enumerate(subgraphs):
avg_degree = sum(subgraph.degree())/len(subgraph.degree())
print(f"The average degree of subgraph {i+1} is: {avg_degree}")
The average degree of subgraph 1 is: 32.87436264680607 The average degree of subgraph 2 is: 2.727272727272727 The average degree of subgraph 3 is: 2.4347826086956523 The average degree of subgraph 4 is: 3.0 The average degree of subgraph 5 is: 3.3333333333333335 The average degree of subgraph 6 is: 1.0 The average degree of subgraph 7 is: 1.3333333333333333 The average degree of subgraph 8 is: 4.909090909090909 The average degree of subgraph 9 is: 1.0 The average degree of subgraph 10 is: 2.0 The average degree of subgraph 11 is: 1.0 The average degree of subgraph 12 is: 1.0 The average degree of subgraph 13 is: 1.0 The average degree of subgraph 14 is: 1.0 The average degree of subgraph 15 is: 1.3333333333333333 The average degree of subgraph 16 is: 1.0 The average degree of subgraph 17 is: 1.0 The average degree of subgraph 18 is: 1.0 The average degree of subgraph 19 is: 1.0
The average degree is a measure of the average number of edges connected to each node in the subgraph.
The output shows the results of the calculation for each subgraph. The interpretation of the results is that some subgraphs have a relatively high average degree, such as subgraph 1 with an average degree of 32.87, while others have a very low average degree, such as subgraphs 6, 9, 11, 12, 13, 14, 16, 17, 18, and 19, which all have an average degree of 1.0. This suggests that the nodes in these subgraphs have very few edges compared to the nodes in subgraph 1. The analysis of average degree in subgraphs could help to identify important subgraphs in the network.
for i, sg in enumerate(subgraphs):
print(f"Subgraph {i+1}:")
print(f"Density: {sg.density()}")
print(f"Triangles: {len(sg.cliques(min=3, max=3))}")
print(f"Modularity: {sg.modularity(sg.community_fastgreedy().as_clustering())}")
print(f"Eccentricity: {max(sg.eccentricity())}")
print(f"Assortativity: {sg.assortativity_degree(directed=False)}")
print(f"Clique Number: {sg.clique_number()}")
print("------------------------------")
Subgraph 1: Density: 0.0018834858855738552 Triangles: 3547243 Modularity: 0.7878293538160316 Eccentricity: 16.0 Assortativity: 0.29305462580433556 Clique Number: 143 ------------------------------ Subgraph 2: Density: 0.12987012987012986 Triangles: 8 Modularity: 0.48444444444444446 Eccentricity: 8.0 Assortativity: -0.07816711590296527 Clique Number: 4 ------------------------------ Subgraph 3: Density: 0.1106719367588933 Triangles: 1 Modularity: 0.49298469387755095 Eccentricity: 6.0 Assortativity: -0.2822545134302065 Clique Number: 3 ------------------------------ Subgraph 4: Density: 1.0 Triangles: 4 Modularity: 0.0 Eccentricity: 1.0 Assortativity: nan Clique Number: 4 ------------------------------ Subgraph 5: Density: 0.10416666666666667 Triangles: 15 Modularity: 0.4907438016528925 Eccentricity: 8.0 Assortativity: 0.1029774322017813 Clique Number: 4 ------------------------------ Subgraph 6: Density: 1.0 Triangles: 0 Modularity: 0.0 Eccentricity: 1.0 Assortativity: nan Clique Number: 2 ------------------------------ Subgraph 7: Density: 0.6666666666666666 Triangles: 0 Modularity: 0.0 Eccentricity: 2.0 Assortativity: -1.0 Clique Number: 2 ------------------------------ Subgraph 8: Density: 0.4909090909090909 Triangles: 34 Modularity: 0.32853223593964337 Eccentricity: 4.0 Assortativity: 0.8341862845445241 Clique Number: 6 ------------------------------ Subgraph 9: Density: 1.0 Triangles: 0 Modularity: 0.0 Eccentricity: 1.0 Assortativity: nan Clique Number: 2 ------------------------------ Subgraph 10: Density: 1.0 Triangles: 1 Modularity: 0.0 Eccentricity: 1.0 Assortativity: nan Clique Number: 3 ------------------------------ Subgraph 11: Density: 1.0 Triangles: 0 Modularity: 0.0 Eccentricity: 1.0 Assortativity: nan Clique Number: 2 ------------------------------ Subgraph 12: Density: 1.0 Triangles: 0 Modularity: 0.0 Eccentricity: 1.0 Assortativity: nan Clique Number: 2 ------------------------------ Subgraph 13: Density: 1.0 Triangles: 0 Modularity: 0.0 Eccentricity: 1.0 Assortativity: nan Clique Number: 2 ------------------------------ Subgraph 14: Density: 1.0 Triangles: 0 Modularity: 0.0 Eccentricity: 1.0 Assortativity: nan Clique Number: 2 ------------------------------ Subgraph 15: Density: 0.6666666666666666 Triangles: 0 Modularity: 0.0 Eccentricity: 2.0 Assortativity: -1.0 Clique Number: 2 ------------------------------ Subgraph 16: Density: 1.0 Triangles: 0 Modularity: 0.0 Eccentricity: 1.0 Assortativity: nan Clique Number: 2 ------------------------------ Subgraph 17: Density: 1.0 Triangles: 0 Modularity: 0.0 Eccentricity: 1.0 Assortativity: nan Clique Number: 2 ------------------------------ Subgraph 18: Density: 1.0 Triangles: 0 Modularity: 0.0 Eccentricity: 1.0 Assortativity: nan Clique Number: 2 ------------------------------ Subgraph 19: Density: 1.0 Triangles: 0 Modularity: 0.0 Eccentricity: 1.0 Assortativity: nan Clique Number: 2 ------------------------------
Density:A measure of how well connected the nodes are in a graph. It is defined as the ratio of the number of edges in the graph to the maximum number of edges possible in a graph with the same number of nodes.The density of a graph can range from 0 to 1, where a density of 0 means that there are no edges in the graph, and a density of 1 means that every possible edge is present in the graph.In general, a higher density can indicate a more connected and cohesive network, while a lower density can indicate a more sparse or fragmented network. However, the interpretation can vary depending on the specific application and the characteristics of the graph. As you can see subgraph 1 is very low connected due to the big network it has
Triangle count for each subgraph, which is the number of cycles of length 3 in the graph. It is an important metric for measuring the level of connectivity between nodes in a graph, and is related to the concept of clustering.From the output, we can see that the largest subgraph (Subgraph 1) has a low density, meaning that it is relatively sparse, but it has a high number of triangles, indicating a high level of clustering among the nodes. In contrast, some of the smaller subgraphs have a higher density, but a lower number of triangles, suggesting a lower level of clustering.
Modularity: A measure of the density of links within modules or communities in the graph. A modularity of 0 indicates that the graph is random, whereas a modularity of 1 indicates that the graph is completely modular. In this case, the subgraph 1 has a high modularity of 0.8, indicating that the nodes are densly connected and for the other subgraphs are not organized into distinct communities.
Eccentricity: The maximum distance between a node and all other nodes in the subgraph. In Subgraph 1, the maximum distance is 16, which means that the subgraph is relatively large and spread out, which some nodes far apart. In comparison to the others with 1 are directly connected.
Assortativity: This measures the degree to which nodes in the subgraph tend to be connected to other nodes with a similar degree. A value of 0.293 suggests that there is some tendency for nodes in subgraph 1 to be connected to other nodes with a similar degree, but it is not a very strong effect
Clique Number: This is the size of the largest clique (fully connected subgraph) within the subgraph 1. A value of 143 suggests that there is a very large clique within this subgraph, which may indicate the presence of a highly connected group of nodes, while the other graohs are very low in number e.g of 2 suggest the largest clique consists of two nodes.
Weight is an important factor in measuring the closeness between two nodes because it takes into account the strength of the connection between them. In a weighted graph, the closer two nodes are, the higher their weight is, indicating a stronger connection between them.
Weight also affects the calculation of Betweenness centrality, which measures the importance of a node in terms of the number of shortest paths that pass through it. If a weighted graph has high-weight edges, then the betweenness centrality of nodes that are on those edges will be higher than the nodes that are on low-weight edges.
Similarly, the impact of weight on the PageRank algorithm, which measures the importance of nodes in a network based on the number and quality of links to them. In a weighted graph, the PageRank of a node that is connected to high-weight nodes will be higher than that of a node that is connected to low-weight nodes.
Weight also affects the calculation of Eigenvector centrality, which measures the importance of a node based on its connections to other important nodes in the network. In a weighted graph, a node that is connected to high-weight nodes will have a higher eigenvector centrality than a node that is connected to low-weight nodes.
Weight is also an important factor in measuring the closeness of nodes in a graph. In a weighted graph, the closeness between two nodes takes into account the weight of the edges connecting them, which means that nodes that are connected by high-weight edges will have a smaller distance between them than nodes that are connected by low-weight edges.
Coreness: A measure of the level of connectedness of the nodes in the subgraph, where nodes with a higher coreness value are more strongly connected to other nodes in the subgraph, no weight measure is here needed.
# Calculate the degree, betweenness centrality, pagerank, eigenvector centrality, and closeness centrality
#and use for the one the inverse graph since we want to display the relationship correctly
#degree = g_inv.degree(directed =False)
betweenness = g_inv.betweenness(weights='weight', directed =False, cutoff=None)
pagerank = g_inv.pagerank(weights='weight',directed =False)
eigenvector = g_inv.eigenvector_centrality(weights='weight',directed =False)
closeness = g_inv.closeness(weights='weight', normalized=True, mode= "all",vertices= g_inv.vs)
# Add the calculated metrics to the node dataframe
#df_with_genres['degree'] = degree
df_with_genres['betweenness_weigth'] = betweenness
df_with_genres['pagerank_weigth'] = pagerank
df_with_genres['eigenvector_weight'] = eigenvector
df_with_genres['closeness_weight'] = closeness
# Add a column with the neighbors for each node
neighbors = [g_inv.neighbors(node_id) for node_id in g_inv.vs['name']]
df_with_genres['neighbors'] = neighbors
df_with_genres.sort_values(by='betweenness_weigth', ascending=False).head(1)
| id | ActorName | movies_95_04 | main_genre | Music | Animation | Action | Crime | Horror | Comedy | Adult | Documentary | Drama | Romance | Sci-Fi | Thriller | Western | Mystery | NULL | Family | Short | Fantasy | Adventure | War | Musical | betweenness_weigth | pagerank_weigth | eigenvector_weight | closeness_weight | neighbors | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 12147 | n162 | Davis, Mark (V) | 540 | Adult | 0 | 0 | 1 | 1 | 0 | 3 | 429 | 5 | 6 | 0 | 1 | 0 | 0 | 0 | 92 | 0 | 1 | 1 | 0 | 0 | 0 | 1.703766e+07 | 0.000427 | 0.980388 | 0.835476 | [83, 86, 104, 131, 137, 139, 142, 181, 184, 29... |
# Calculate the degree, betweenness centrality, pagerank, eigenvector centrality, and closeness centrality
#and use for the one the inverse graph since we want to display the relationship correctly
degree = g.degree()
betweenness = g.betweenness(directed =False)
closeness = g.closeness(normalized= True, mode= "all",vertices= g.vs)
coreness = g.coreness()
# Add the calculated metrics to the node dataframe
df_with_genres['degree'] = degree
df_with_genres['betweenness'] = betweenness
df_with_genres['closeness'] = closeness
df_with_genres['coreness']= coreness
df_with_genres.sort_values(by='betweenness', ascending=False).head(1)
| id | ActorName | movies_95_04 | main_genre | Music | Animation | Action | Crime | Horror | Comedy | Adult | Documentary | Drama | Romance | Sci-Fi | Thriller | Western | Mystery | NULL | Family | Short | Fantasy | Adventure | War | Musical | betweenness_weigth | pagerank_weigth | eigenvector_weight | closeness_weight | neighbors | degree | betweenness | closeness | coreness | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 10548 | n2108 | Jeremy, Ron | 280 | Adult | 2 | 1 | 0 | 0 | 9 | 15 | 149 | 26 | 15 | 3 | 8 | 3 | 0 | 0 | 43 | 0 | 4 | 0 | 1 | 0 | 1 | 1.487206e+07 | 0.000465 | 0.752032 | 0.836407 | [83, 104, 131, 183, 199, 216, 301, 345, 365, 4... | 471 | 9.748544e+06 | 0.28272 | 77 |
Add the subgraph number to the dataframe
subgraph_num_list = []
for i in range(len(df_with_genres)):
node_id = df_with_genres.iloc[i]['id']
for j, subgraph in enumerate(subgraphs):
if node_id in subgraph.vs['name']:
subgraph_num_list.append(j)
break
df_with_genres['subgraph_num'] = subgraph_num_list
df_with_genres.sort_values(by='betweenness', ascending=False).head()
| id | ActorName | movies_95_04 | main_genre | Music | Animation | Action | Crime | Horror | Comedy | Adult | Documentary | Drama | Romance | Sci-Fi | Thriller | Western | Mystery | NULL | Family | Short | Fantasy | Adventure | War | Musical | betweenness_weigth | pagerank_weigth | eigenvector_weight | closeness_weight | neighbors | degree | betweenness | closeness | coreness | subgraph_num | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 10548 | n2108 | Jeremy, Ron | 280 | Adult | 2 | 1 | 0 | 0 | 9 | 15 | 149 | 26 | 15 | 3 | 8 | 3 | 0 | 0 | 43 | 0 | 4 | 0 | 1 | 0 | 1 | 1.487206e+07 | 0.000465 | 7.520318e-01 | 0.836407 | [83, 104, 131, 183, 199, 216, 301, 345, 365, 4... | 471 | 9.748544e+06 | 0.282720 | 77 | 0 |
| 4693 | n3284 | Chan, Jackie (I) | 59 | Comedy | 1 | 0 | 2 | 4 | 1 | 13 | 0 | 18 | 0 | 2 | 0 | 3 | 0 | 0 | 4 | 3 | 7 | 1 | 0 | 0 | 0 | 5.217806e+06 | 0.000215 | 2.028330e-03 | 0.837658 | [67, 228, 269, 732, 775, 855, 926, 1090, 1145,... | 135 | 4.716909e+06 | 0.287238 | 51 | 0 |
| 2563 | n564 | Cruz, Penélope | 46 | Drama | 3 | 0 | 0 | 0 | 0 | 2 | 0 | 5 | 6 | 6 | 0 | 2 | 3 | 2 | 3 | 5 | 5 | 0 | 1 | 3 | 0 | 9.200956e+06 | 0.000270 | 2.370663e-03 | 0.865430 | [25, 59, 67, 147, 368, 586, 617, 708, 799, 926... | 182 | 4.330663e+06 | 0.295555 | 56 | 0 |
| 14433 | n14458 | Shahlavi, Darren | 16 | Action | 0 | 0 | 4 | 0 | 1 | 3 | 0 | 1 | 1 | 0 | 1 | 3 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 4.285954e+06 | 0.000040 | 2.510683e-07 | 0.500483 | [2041, 2738, 3193, 4487, 14424, 14526, 15326, ... | 8 | 4.295503e+06 | 0.193886 | 6 | 0 |
| 15720 | n17308 | Del Rosario, Monsour | 20 | Action | 0 | 0 | 8 | 0 | 2 | 0 | 0 | 0 | 3 | 1 | 1 | 2 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 1 | 0 | 4.267096e+06 | 0.000029 | 2.301942e-09 | 0.402604 | [7672, 10735, 14433, 15280, 15295, 15989] | 6 | 4.267099e+06 | 0.163154 | 5 | 0 |
# Print each subgraph to a PNG file
for i, subgraph in enumerate(subgraphs):
filename = f"subgraph_{i+1}.png"
subgraph.vs['label'] = subgraph.vs['name']
ig.plot(subgraph, target=filename)
Only the most different graphs are shows, the subgraphs which are missing are literally only two nodes like subgraph 6.
This section shows degree distribution by counting the number of vertices with each degree. It prints the degree distribution for each subgraph
Degree distribution is a measure of the frequency of nodes with a certain degree in a network. The weight of the edges is not used in the calculation of degree distribution, as it only considers the number of edges connected to a node, i.e., the degree of the node. Therefore, weight is not used in degree distribution
# Iterate over each subgraph
for i, subgraph in enumerate(subgraphs):
# Compute the degree sequence
degree_sequence = subgraph.degree()
# Compute the degree distribution
degree_count = {}
for d in degree_sequence:
degree_count[d] = degree_count.get(d, 0) + 1
# Print the degree distribution
print(f"Subgraph {i+1} degree distribution:", degree_count)
Subgraph 1 degree distribution: {36: 109, 23: 230, 22: 273, 46: 90, 18: 353, 5: 649, 6: 656, 57: 42, 11: 521, 7: 659, 93: 22, 47: 85, 25: 235, 19: 316, 107: 14, 1: 434, 8: 583, 123: 13, 78: 24, 74: 46, 34: 158, 24: 231, 30: 161, 2: 572, 26: 209, 13: 411, 16: 377, 21: 291, 9: 604, 51: 71, 29: 209, 66: 44, 56: 60, 3: 609, 84: 26, 61: 49, 102: 14, 31: 180, 4: 670, 28: 202, 12: 485, 145: 9, 15: 425, 14: 404, 289: 3, 83: 31, 92: 23, 37: 104, 10: 553, 44: 96, 43: 97, 345: 2, 27: 222, 45: 93, 54: 53, 160: 8, 174: 4, 118: 8, 144: 7, 20: 284, 105: 21, 97: 22, 77: 40, 32: 168, 59: 56, 104: 20, 90: 21, 33: 141, 146: 10, 87: 26, 52: 66, 70: 35, 49: 79, 71: 39, 53: 72, 86: 23, 82: 18, 35: 133, 60: 53, 81: 34, 181: 13, 17: 328, 68: 50, 184: 9, 162: 7, 94: 24, 39: 115, 179: 9, 41: 118, 147: 9, 50: 80, 216: 4, 40: 127, 180: 6, 42: 92, 190: 6, 241: 2, 157: 6, 131: 9, 55: 63, 80: 31, 63: 61, 99: 22, 194: 6, 76: 31, 140: 10, 125: 9, 392: 1, 438: 1, 38: 114, 79: 33, 73: 30, 72: 39, 58: 46, 231: 4, 65: 41, 48: 76, 166: 9, 115: 18, 111: 18, 124: 13, 225: 8, 259: 3, 161: 3, 127: 21, 158: 5, 85: 24, 96: 21, 222: 4, 75: 51, 355: 2, 101: 24, 129: 5, 254: 3, 153: 5, 220: 4, 211: 6, 133: 4, 120: 14, 69: 48, 142: 8, 207: 12, 334: 2, 64: 47, 113: 15, 188: 4, 263: 3, 134: 14, 333: 1, 103: 15, 151: 7, 450: 1, 275: 4, 165: 7, 154: 6, 95: 21, 182: 5, 186: 7, 138: 5, 98: 28, 189: 7, 109: 16, 218: 4, 149: 10, 203: 10, 171: 7, 110: 15, 62: 53, 128: 12, 91: 18, 187: 9, 163: 9, 177: 9, 610: 1, 185: 8, 89: 23, 121: 17, 297: 1, 108: 14, 267: 1, 318: 2, 143: 4, 287: 2, 176: 5, 376: 3, 88: 19, 168: 9, 239: 1, 219: 4, 295: 1, 67: 26, 100: 14, 183: 8, 119: 21, 340: 1, 533: 1, 116: 8, 173: 2, 106: 11, 114: 21, 117: 12, 130: 6, 137: 8, 245: 3, 122: 8, 159: 8, 206: 8, 126: 7, 148: 5, 214: 5, 402: 1, 132: 8, 164: 4, 172: 7, 210: 7, 156: 2, 135: 7, 175: 8, 251: 2, 410: 1, 361: 1, 136: 8, 196: 5, 280: 3, 178: 7, 221: 7, 246: 1, 195: 4, 213: 4, 191: 7, 561: 1, 255: 1, 457: 1, 200: 3, 167: 6, 204: 6, 139: 8, 265: 3, 366: 1, 293: 1, 208: 6, 152: 3, 236: 1, 227: 4, 169: 5, 286: 1, 346: 2, 112: 12, 201: 5, 232: 2, 500: 1, 417: 1, 256: 1, 197: 2, 192: 7, 545: 1, 310: 1, 212: 7, 224: 3, 199: 6, 215: 4, 223: 2, 451: 1, 240: 3, 228: 1, 217: 2, 209: 6, 463: 1, 400: 1, 262: 3, 299: 1, 193: 5, 341: 1, 205: 6, 155: 4, 257: 3, 253: 3, 202: 4, 229: 3, 395: 2, 313: 1, 555: 1, 283: 2, 320: 2, 141: 4, 471: 2, 323: 1, 324: 2, 198: 3, 419: 1, 336: 1, 398: 1, 377: 1, 244: 2, 335: 1, 170: 3, 427: 1, 278: 1, 272: 2, 269: 1, 319: 1, 294: 1, 584: 1, 298: 1, 428: 1, 150: 2, 226: 2, 233: 1, 260: 2, 784: 1, 393: 2, 252: 2, 312: 1, 599: 1, 243: 1, 266: 1, 331: 1, 382: 1, 258: 2, 326: 1, 372: 2, 270: 2, 242: 1, 416: 1, 314: 2, 365: 1, 358: 1, 493: 1, 235: 1, 279: 1, 311: 1, 490: 1, 350: 2, 250: 1, 475: 1, 300: 1}
Subgraph 2 degree distribution: {3: 7, 8: 1, 4: 5, 1: 7, 2: 2}
Subgraph 3 degree distribution: {2: 10, 3: 4, 1: 5, 4: 3, 7: 1}
Subgraph 4 degree distribution: {3: 4}
Subgraph 5 degree distribution: {5: 5, 3: 7, 4: 4, 7: 1, 6: 2, 1: 5, 2: 8, 8: 1}
Subgraph 6 degree distribution: {1: 2}
Subgraph 7 degree distribution: {2: 1, 1: 2}
Subgraph 8 degree distribution: {6: 6, 5: 1, 4: 1, 3: 3}
Subgraph 9 degree distribution: {1: 2}
Subgraph 10 degree distribution: {2: 3}
Subgraph 11 degree distribution: {1: 2}
Subgraph 12 degree distribution: {1: 2}
Subgraph 13 degree distribution: {1: 2}
Subgraph 14 degree distribution: {1: 2}
Subgraph 15 degree distribution: {1: 2, 2: 1}
Subgraph 16 degree distribution: {1: 2}
Subgraph 17 degree distribution: {1: 2}
Subgraph 18 degree distribution: {1: 2}
Subgraph 19 degree distribution: {1: 2}
# Get the subgraphs
subgraphs = g.decompose()
# Create a figure for the plot
fig = go.Figure()
# Iterate over each subgraph
for i, subgraph in enumerate(subgraphs):
# Get the degree distribution
degree_dist = subgraph.degree_distribution()
# Convert the degree distribution to a list of tuples
degree_dist = [(bin_left, count) for bin_left, _, count in degree_dist.bins()]
# Sort the list of tuples by the bin left value
degree_dist = sorted(degree_dist, key=lambda x: x[0])
# Add a trace for the degree distribution of the current subgraph
fig.add_trace(go.Scatter(x=[x[0] for x in degree_dist], y=[x[1] for x in degree_dist], mode='lines', name=f'Subgraph {i+1}'))
# Update the layout
#fig.update_layout(xaxis_title='Degree', yaxis_title='Count', title='Degree distribution per subgraph')
# Show the plot
#fig.show()
The degree distribution plot shows how many connections (or edges) each actor has in the network. On the x-axis, we have the degree of the nodes, and on the y-axis, we have the number of actors with that degree.
In Subgraph 1 (blue line), we can observe that most actors have a small number of connections, with a peak of 670 actors having only 4 degrees/ connection. The plot then drops sharply, indicating that there are relatively few actors with more than few connections/degrees.
The plot reaches its maximum at a degree of 784, where only one actor is present, meaning that this actor is highly connected in the network. This kind of plot can help us understand the overall structure of the network and identify the most influential actors (i.e., those with the highest degree).
Subgraph 5 & 3& 2: Shows also a declining trend from low degrees to the highest degree of 8 & 7.
Subgraph 8: Degree is increasing from degree 4 to degree 6
# iterate over each subgraph and find min and max degree
for i, sg in enumerate(subgraphs):
min_degree = min(sg.degree())
max_degree = max(sg.degree())
print(f"The maximum degree of subgraph {i} is: {max_degree}")
The maximum degree of subgraph 0 is: 784 The maximum degree of subgraph 1 is: 8 The maximum degree of subgraph 2 is: 7 The maximum degree of subgraph 3 is: 3 The maximum degree of subgraph 4 is: 8 The maximum degree of subgraph 5 is: 1 The maximum degree of subgraph 6 is: 2 The maximum degree of subgraph 7 is: 6 The maximum degree of subgraph 8 is: 1 The maximum degree of subgraph 9 is: 2 The maximum degree of subgraph 10 is: 1 The maximum degree of subgraph 11 is: 1 The maximum degree of subgraph 12 is: 1 The maximum degree of subgraph 13 is: 1 The maximum degree of subgraph 14 is: 2 The maximum degree of subgraph 15 is: 1 The maximum degree of subgraph 16 is: 1 The maximum degree of subgraph 17 is: 1 The maximum degree of subgraph 18 is: 1
# iterate over each subgraph and find min and max degree
for i, sg in enumerate(subgraphs):
min_degree = min(sg.degree())
max_degree = max(sg.degree())
print(f"The minimum degree of subgraph {i} is: {min_degree}")
The minimum degree of subgraph 0 is: 1 The minimum degree of subgraph 1 is: 1 The minimum degree of subgraph 2 is: 1 The minimum degree of subgraph 3 is: 3 The minimum degree of subgraph 4 is: 1 The minimum degree of subgraph 5 is: 1 The minimum degree of subgraph 6 is: 1 The minimum degree of subgraph 7 is: 3 The minimum degree of subgraph 8 is: 1 The minimum degree of subgraph 9 is: 2 The minimum degree of subgraph 10 is: 1 The minimum degree of subgraph 11 is: 1 The minimum degree of subgraph 12 is: 1 The minimum degree of subgraph 13 is: 1 The minimum degree of subgraph 14 is: 1 The minimum degree of subgraph 15 is: 1 The minimum degree of subgraph 16 is: 1 The minimum degree of subgraph 17 is: 1 The minimum degree of subgraph 18 is: 1
The maxium degree are 784 connection/ degree to other nodes and the minimum is 1 degree only.
# calculate the degrees of all nodes
degrees = g.degree()
# create a DataFrame from the dictionary
df_degrees = pd.DataFrame({'node': g.vs['name'], 'degree': degrees})
# get the maximum degree and corresponding actors
max_degree = df_degrees['degree'].max()
actors_with_max_degree = df_degrees.loc[df_degrees['degree'] == max_degree, 'node'].tolist()
# get the minimum degree and corresponding actors
min_degree = df_degrees['degree'].min()
actors_with_min_degree = df_degrees.loc[df_degrees['degree'] == min_degree, 'node'].tolist()
# print the results
print("Actors with the maximum degree:", actors_with_max_degree)
print("Actors with the minimum degree:", actors_with_min_degree)
Actors with the maximum degree: ['n162'] Actors with the minimum degree: ['n9764', 'n6151', 'n17479', 'n10577', 'n11616', 'n14370', 'n15989', 'n1479', 'n17623', 'n14631', 'n16224', 'n11477', 'n17093', 'n8316', 'n11572', 'n12592', 'n17250', 'n15576', 'n11977', 'n7607', 'n7063', 'n15803', 'n12596', 'n3698', 'n2028', 'n3838', 'n8809', 'n11452', 'n7742', 'n2105', 'n17800', 'n12164', 'n13888', 'n14870', 'n7186', 'n11369', 'n17860', 'n9128', 'n6979', 'n4888', 'n10942', 'n17388', 'n10120', 'n16362', 'n17633', 'n13435', 'n16661', 'n17915', 'n10967', 'n12244', 'n14848', 'n11592', 'n15747', 'n15782', 'n9182', 'n10569', 'n7468', 'n2802', 'n4250', 'n15115', 'n7937', 'n9378', 'n11606', 'n14444', 'n4380', 'n7017', 'n12264', 'n13660', 'n13327', 'n17842', 'n17910', 'n15839', 'n15744', 'n17882', 'n16829', 'n16888', 'n8841', 'n11550', 'n17533', 'n17447', 'n16072', 'n4049', 'n8421', 'n8454', 'n5718', 'n10394', 'n4976', 'n10691', 'n6746', 'n6587', 'n17477', 'n15221', 'n13904', 'n17242', 'n4082', 'n15810', 'n6367', 'n17807', 'n13653', 'n7395', 'n9301', 'n10677', 'n16492', 'n14930', 'n17406', 'n11855', 'n13498', 'n11424', 'n17708', 'n7439', 'n14262', 'n13008', 'n11858', 'n10869', 'n15586', 'n15121', 'n2818', 'n11364', 'n16120', 'n17295', 'n7793', 'n15667', 'n10277', 'n13082', 'n13924', 'n7438', 'n2009', 'n13790', 'n9922', 'n16376', 'n10150', 'n11006', 'n1229', 'n10824', 'n393', 'n16799', 'n15916', 'n16369', 'n7575', 'n14745', 'n9516', 'n16250', 'n883', 'n4892', 'n17410', 'n13885', 'n11421', 'n5530', 'n6383', 'n6902', 'n8118', 'n15316', 'n3640', 'n12123', 'n17859', 'n8842', 'n15479', 'n3011', 'n10167', 'n3861', 'n15953', 'n12156', 'n2048', 'n12909', 'n2131', 'n3797', 'n7292', 'n8050', 'n3822', 'n16606', 'n2823', 'n16766', 'n14123', 'n17903', 'n17901', 'n13828', 'n17076', 'n17822', 'n6396', 'n13967', 'n5586', 'n15819', 'n12223', 'n14945', 'n5664', 'n2057', 'n17746', 'n17855', 'n14440', 'n8100', 'n17848', 'n4645', 'n16760', 'n16367', 'n17771', 'n13049', 'n1486', 'n2130', 'n17514', 'n17425', 'n8866', 'n17841', 'n14250', 'n7792', 'n15273', 'n5084', 'n13853', 'n8902', 'n16149', 'n9297', 'n16668', 'n6989', 'n17852', 'n14944', 'n7594', 'n8092', 'n3588', 'n8344', 'n9176', 'n16360', 'n17395', 'n7016', 'n9298', 'n17575', 'n10975', 'n15816', 'n469', 'n13368', 'n10108', 'n12385', 'n15186', 'n13125', 'n6380', 'n214', 'n12303', 'n3151', 'n2821', 'n14866', 'n8926', 'n13292', 'n1461', 'n2065', 'n13802', 'n16943', 'n5982', 'n10319', 'n17008', 'n10246', 'n16412', 'n10976', 'n12162', 'n11262', 'n9958', 'n17139', 'n12781', 'n1462', 'n15008', 'n11951', 'n16805', 'n13394', 'n9159', 'n2808', 'n17911', 'n5793', 'n17409', 'n8346', 'n7355', 'n11091', 'n12693', 'n449', 'n9066', 'n17356', 'n12151', 'n14533', 'n15642', 'n17569', 'n13389', 'n4981', 'n9163', 'n2763', 'n10589', 'n16525', 'n6906', 'n16279', 'n14125', 'n17535', 'n1286', 'n16069', 'n16995', 'n6957', 'n13654', 'n17434', 'n15693', 'n17391', 'n4445', 'n14486', 'n8466', 'n15924', 'n11229', 'n11086', 'n16246', 'n17214', 'n17440', 'n9429', 'n17820', 'n10766', 'n15231', 'n17775', 'n15933', 'n16916', 'n15173', 'n515', 'n8804', 'n583', 'n14858', 'n17411', 'n17854', 'n7103', 'n10084', 'n17613', 'n13317', 'n4041', 'n12127', 'n1500', 'n17847', 'n6828', 'n16340', 'n4902', 'n16965', 'n17105', 'n17734', 'n10837', 'n14871', 'n11466', 'n4242', 'n5947', 'n5866', 'n8410', 'n14400', 'n5490', 'n4044', 'n16408', 'n8463', 'n17635', 'n14102', 'n9220', 'n17801', 'n8816', 'n16370', 'n17200', 'n17723', 'n17865', 'n12193', 'n17750', 'n12899', 'n16631', 'n8419', 'n16913', 'n14637', 'n15226', 'n9236', 'n15578', 'n15769', 'n16494', 'n7038', 'n10243', 'n13260', 'n14590', 'n2841', 'n14269', 'n9749', 'n17834', 'n11126', 'n8512', 'n11610', 'n7713', 'n16469', 'n14121', 'n10379', 'n9401', 'n13295', 'n15665', 'n17809', 'n14006', 'n10733', 'n17883', 'n2107', 'n13706', 'n14246', 'n11576', 'n4239', 'n4395', 'n1544', 'n6860', 'n14582', 'n11753', 'n12222', 'n5581', 'n14754', 'n13393', 'n17390', 'n15059', 'n13582', 'n3567', 'n13649', 'n9253', 'n473', 'n10153', 'n15951', 'n8098', 'n12153', 'n15557', 'n17618', 'n7505', 'n11149', 'n15931', 'n16567', 'n7364', 'n9672', 'n4974', 'n10157', 'n17196', 'n5713', 'n17797', 'n248', 'n6999', 'n17904', 'n7453', 'n8763', 'n16495', 'n13638', 'n14022', 'n11288', 'n16718', 'n13334', 'n17884', 'n17737', 'n15773', 'n8081', 'n496', 'n11312', 'n15571', 'n10648', 'n13628', 'n17907', 'n13859', 'n7449', 'n13128', 'n16116', 'n14088', 'n10822', 'n15812', 'n1250', 'n16583', 'n12865', 'n14459', 'n3138', 'n5739', 'n15495', 'n17298', 'n14111', 'n8868', 'n8037', 'n7311', 'n16226', 'n16272', 'n7093', 'n17740', 'n14457', 'n10268', 'n13307', 'n16798', 'n13581', 'n8501', 'n3616']
# calculate the degree of each node
degrees = g.degree()
# find the node with the minimum degree
min_degree = min(degrees)
min_nodes = [i for i, deg in enumerate(degrees) if deg == min_degree]
# find the node with the maximum degree
max_degree = max(degrees)
max_nodes = [i for i, deg in enumerate(degrees) if deg == max_degree]
# print the names of the actors with minimum and maximum degrees
min_actors = nodes.iloc[min_nodes]['ActorName'].tolist()
max_actors = nodes.iloc[max_nodes]['ActorName'].tolist()
print("Actors with minimum degree:", min_actors)
print("Actors with maximum degree:", max_actors)
Actors with minimum degree: ['Sokoloff, Marla', 'Bonham-Carter, Crispin', 'Nagatsuka, Kyozo', 'R'Mante, Adrian', 'Moore, Rudy Ray', 'Young, Dey', 'Lemche, Kris', 'Schönemann, Hinnerk', 'Peluffo, Mariano', 'Ulliel, Gaspard', 'Offerman, Nick', 'Rieck, Billy', 'Busse, John', 'Krusiec, Michelle', 'Baruc, Siri', 'Collins, Paul (I)', 'Peterson, Cassandra', 'Kurtiz, Tuncel', 'Bossa, Luly', 'de Mylius, Jørgen', 'Newton, Wayne', 'Stuart, James Patrick', 'Julien, Jean-Luc', 'Douglas, Shirley', 'Michael, Christopher', 'Davis, Sarah Scott', 'Van Wormer, Steve', 'Dempsey, Michael (I)', 'Zehetner, Nora', 'Romeo, Marc', 'Ravitz, Nati', 'Jennings, Brent', 'Jacot, Christopher', 'Mylan, Richard', 'Polk, Stephen', 'Ryan, Thomas Jay', 'Nørbygård, Finn', 'Leroux, Maxime', 'Drake, David (I)', 'Cooper, Rowena', 'Blanche, Robert', 'Karsenti, Sabine', 'Fernandez, Peter (I)', 'Wu, Robert', 'Shakibai, Khosro', 'Söllner, Pippi', 'Bomonde, Betty', 'Polonia, John', 'Hoffman, Jackie', 'Werner, Roy', 'Godboldo, Dale', 'Gaffney, Mo', 'Russom, Leon', 'Blanks, Billy', 'Limas, Jim Adhi', 'Schwartzman, Jason', 'Sec, Frantisek', 'Needham, Tracey', 'De Bankolé, Isaach', 'Calzone, Maria Pia', 'Vukotic, Milena', 'Westfeldt, Jennifer', 'Williams, Lia', 'Bolkan, Florinda', 'von Franckenstein, Clement', 'Quinn, Francesco', 'Karven, Ursula', 'Cordy, Annie', 'Dumaurier, Francis', 'Guskov, Aleksei', 'Wynn, Anthony', 'Neale, Brent', 'Holt, David (III)', 'Walsh, Darren (I)', 'McManus, Rove', 'Sterling, Rachel', 'Shimono, Sab', 'Brener, Shirly', 'Cha, Seung-won', 'Yankovsky, Oleg', 'Azizi, Anthony', 'Fox, Emilia', 'Landry, Ali', 'Robinson, Andrew (I)', 'Anthony, Lysette', 'Canals, Maria', 'Kober, Jeff', 'Babcock, Todd', 'Kerman, Ken', 'Krauss, Naomi', 'Tokiwa, Takako', 'Thomas, Naím', 'Mills, Judson', 'Tjalsma, Joke', 'McMurtry, Michael', 'Kirk, Justin', 'Agenin, Béatrice', 'Levy, Nir (I)', 'Sola, Catherine', 'Rogan, Joe', 'Strozier, Henry', 'Bennett, Fran (I)', 'Garner, Kelli', 'Carroll, Justin', 'Alvarez, Juan Luis', 'Wildbolz, Klaus', 'Fallenstein, Karina', 'Walker, Polly (II)', 'Mims, Roxzane T.', 'Brady, Orla', 'Maddox, Billy', 'Damien (III)', 'Ullmann, Kostja', 'Lawrence, Sharon', 'Gibbons, Leeza', 'Lawson, Denis', 'Craig, Andrew (I)', 'Chamberlin, Kevin', 'Matsushita, Yuki', 'Bubber', 'Stover, George', 'Johnson, Rick (I)', 'Pickles, Carolyn', 'Cannatella, Trishelle', 'Franek, Ivan', 'Petrucci, Luigi (I)', 'Cutzarida, Ivo', 'Moritz, Dorothea', 'Caparrós, Alonso', 'Riemelt, Max', 'Yenque, Jose', 'Ellis, Aunjanue', 'Kae-Kazim, Hakeem', 'Buendía, Rafael', 'Langton, Brooke', 'Mailhouse, Robert', 'Desverchère, Jocelyne', 'Barry, Rod', 'Linden, Hal', 'Deret, Jean-Claude', 'Berman, Andy', 'Abbas, Hiyam', 'Alexander, Flex', 'Reid, Mike (I)', 'Tamura, Eriko', 'Caldwell, L. Scott', 'Loughlin, Lori', 'Brennan, Eileen', 'Parfitt, Judy', 'Hipp, Paul (I)', 'Mari, Gina', 'Miller, Kristen', 'Coogan, Keith', 'Lyles, Leslie', 'Soriat, Bettina', 'Flaherty, Lanny', 'Rosen, Beatrice', 'Havers, Nigel', 'Kiely, Mark', 'Kraljevic, Ivan', 'Baker, George (I)', 'Rodríguez, Marco (I)', 'Gossett, Robert', 'Marchelletta, Jeff', 'Jordan, Leslie', 'Jones, January (I)', 'Mitchum, Christopher', 'Elias, Cyrus', 'Howerton, Charles', 'Brady, Moya', 'Dent, Cheryl', 'Rivière, Marie', 'Coulson, Lindsey', 'Lähde, Ville (I)', 'Alexander, Sharon (I)', 'Hayes, David C.', 'Perine, Kelly', 'Lee, Mark (X)', 'Langham, Chris', 'Palmer-Stoll, Julia', 'Marshall, Paula', 'O'Rourke, Shaun (II)', 'Olds, Gabriel', 'Paxton, Sara', 'Theirse, Darryl', 'Eckhouse, James', 'Canton, Joanna', 'Zaki, Mona', 'Abecassis, Yaël', 'Wildman, Valerie', 'Moutsatsou, Katerina', 'Hailer, April', 'Richardson, Sy', 'Wirth, Billy', 'Koklas, Kostas', 'Skye, Ione', 'Heyl, Burkhard', 'Borlenghi, Matt', 'Amandla', 'Malmberg, Claes', 'Martin, Rémi', 'Anderson, Fred (III)', 'Canovas, Anne', 'Chamish, Leanna', 'Gilliard, Carl', 'Bémol, Brigitte', 'Azurdia, Richard', 'Andrei, Lydia', 'Murray, Duane', 'Romanov, Stephanie', 'Perkins, Jack (III)', 'Oxenberg, Catherine', 'Shalabi, Menna', 'Pilato, Joseph', 'Salsedo, Frank', 'Malco, Romany', 'Gould, Geoffrey', 'Chin, Tsai (I)', 'Deshors, Erick', 'Livingston, Richard', 'Hill, Nicholas (I)', 'Taylor, Mark L.', 'Makinen, Karl', 'Dawes, Bill', 'Ritter, Jason', 'Sky, Jennifer', 'Strickland, KaDee', 'Holmes, Teck', 'Katona, Kerry', 'Quinton, Sophie', 'Bremmer, Richard', 'Tortosa, Silvia', 'Donovan, Daisy', 'Bergé, Francine', 'Botone, Talia', 'Tigar, Kenneth', 'McCoy, Andre', 'Macaninch, Cal', 'Lecas, Jean-Claude', 'Lee, Eun-ju (II)', 'Shaw, Vinessa', 'Shonka, J. Scott', 'Hutcherson, Josh', 'Mennegand, Élodie', 'Jade, Claude', 'Zibetti, Roberto', 'Colvin, Shawn', 'Gosling, Ryan (I)', 'Marewski, Armin', 'McCallany, Holt', 'Bergere, Jenica', 'McKinnon, Megan', 'Manilow, Barry', 'Cayo, Fernando', 'Kouyaté, Sotigui', 'Henshaw, Lee', 'Booth, Emily', 'Sciò, Yvonne', 'De Neck, Didier', 'Moffett, D.W.', 'Shimizu, Tsuyu', 'Sarabia, Ric', 'Bang, Eun-jin', 'Watson, Tom (I)', 'Rhys, Ieuan', 'Rasche, David', 'Mealing, Amanda', 'Howard, Ken (I)', 'Witter, Frank', 'Jostyn, Jennifer', 'Sucharetza, Marla', 'Broustal, Sophie', 'Martells, Cynthia', 'Little, Kim', 'Hada, Michiko', 'Yeryomin, Vladimir', 'Russell, Lucy', 'Harrington, Cheryl Francis', 'Koundé, Hubert', 'Schumann, Tanja', 'Shire, Talia', 'Winnick, Katheryn', 'Coll, Ivonne', 'Bonacelli, Paolo', 'Merrells, Jason', 'Choi, Min-su', 'Egan, Peter (I)', 'Taylor, LG', 'Makise, Riho', 'Kean, Greg', 'Albertini, Michel', 'Ruslanova, Nina', 'Chapman, Sean', 'Schaffel, Lauren', 'Burke, Carlease (I)', 'Boutsikaris, Dennis', 'Spano, Vincent', 'Provenza, Paul', 'Wilson, Cal (II)', 'Underwood, Jay (I)', 'Sihol, Caroline', 'Gallagher, Frank (II)', 'Ulyanov, Mikhail', 'Kern, Joey', 'Menti, Nena', 'Milos, Sofia', 'Messuri, LoriDawn', 'Kianian, Reza', 'Idiz, Nurseli', 'Garcia, Aimee', 'Graham, Julie (I)', 'Bron, Eleanor', 'Fitzpatrick, Leo', 'Kasper, Gary', 'Gorski, Tamara', 'Fukaura, Kanako', 'Tork, Hanan', 'Spencer-Nairn, Tara', 'Tweeden, Leeann', 'Hamilton, Emily', 'Ferguson, Tim (I)', 'Owen, Lloyd', 'Drukarova, Dinara', 'Ryan, Mitch', 'Haralambidis, Renos', 'Porizkova, Paulina', 'Zappa, Ahmet', 'Ba, Inday', 'Okada, Yoshinori', 'Ashbourne, Jayne', 'Cass, John (I)', 'Peldon, Ashley', 'Speer, Hugo', 'Cray, Ed', 'Gibney, Susan', 'Czischek, Elke', 'Bohm, Marquard', 'Bergeron, Philippe (I)', 'Rubin, Jennifer', 'Gillette, Anita', 'Thomson, Kristen', 'Gabriela, Marília', 'Davis, William Stanford', 'Vanthielen, Francesca', 'Stewart, Will (I)', 'Seigner, Emmanuelle', 'Hristov, Ivaylo', 'Michaels, Rhino', 'Riker, Robin', 'Georgakis, Nikos', 'Wang, Zhiwen', 'Mizuki, Arisa', 'Rinaldi, Renzo', 'Bal, Kenan', 'Lavandier, Luc', 'Servais, Manuela', 'Lafferty, Sandra Ellis', 'Lim, Kay Tong', 'Zahonero, Coraly', 'Cannon, Harold', 'Smith, Douglas (VI)', 'Speck, Karsten', 'Mojica, Monique', 'Brochtrup, Bill', 'Litz, Nadia', 'Massey, Jennifer (I)', 'May, Roger (I)', 'McMains, Cody', 'Cawood, Sarah', 'Lesure, James', 'Pyper-Ferguson, John', 'Lawlor, Gerri', 'Baraka, Amiri', 'Popowich, Paul', 'Teale, Owen', 'Bellar, Clara', 'Dollar, Aubrey', 'Puleston-Davies, Ian', 'Cryston, Rob', 'Sparber, Herschel', 'Heo, Jun-ho', 'Rosete, Jose', 'Keren, Dror', 'Moyer, Stephen (I)', 'Thornton, Sigrid', 'Pickhaver, Greig', 'O'Toole, Matt', 'Schuch, Karoline', 'Lauren, Val', 'Cozart, Cylk', 'Conti, Tom', 'Hathaway, Amy (I)', 'Ové, Indra', 'Vernon, Kate', 'Clark, Marsha', 'Conde, Fernando', 'Pickett, Cindy', 'Bell, Kristen (I)', 'Pallas, Cécile', 'Gidley, Pamela', 'Praetorius, Friedrich Karl', 'Jett, Joan', 'Fazira, Erra', 'Gordon, Pamela (I)', 'Solka, Gunnar', 'O'Keefe, Michael', 'Glaser, Paul Michael', 'Taylor, Regina (I)', 'Robinson, Charles (I)', 'Guillory, Bennet', 'DeLizia, Cara', 'Moody, Ron', 'Park, Shin-yang', 'Vogt, Paul (II)', 'Cowell, Brendan', 'Alasya, Zeki', 'Franco, Jesus', 'Dapkunaite, Ingeborga', 'Klein, Gérard (I)', 'Montgomery, Judith', 'Ray, Connie', 'Bocher, Christian', 'Kelly, Lisa Robin', 'Bettinger, Manfred', 'Teyssier, Agathe', 'Walter, Lisa Ann', 'Kuusniemi, Matti', 'Malik, Art', 'Steen, Jessica', 'Mangum, Meagan', 'Knaack, Pamela', 'Benesch, Gabriela', 'Bohne, Bruce', 'Gray, Erin', 'Slater, Kelly', 'Moskona, Aryeh', 'Zhou, Xun', 'Timbrook, Corby', 'Winters, Time', 'McIntyre, Marilyn', 'Robinson, Bumper', 'Von Teese, Dita', 'Fields, Edith (I)', 'Sellien, Rainer', 'Harry, 'Horny'', 'Bamman, Gerry', 'Akinnuoye-Agbaje, Adewale', 'Valentín, Juan', 'Saeki, Hinako', 'Shepherd, Suzanne', 'Treviño, Marco Antonio', 'Carol, Jean (I)', 'Wong, Benedict', 'Coppola, Alicia', 'Delfino, Majandra', 'Vincent, Jan-Michael', 'Phillips, Wendy (I)', 'Walker, Ally', 'Ng, Carrie', 'Reema', 'LeBlanc, Sadie', 'Avedikian, Serge', 'Prodan, Andrea', 'Lemke, Anthony', 'Madison, Elina', 'Cao, Jorge', 'Landes, Michael', 'Yoshiyuki, Yumi', 'Janniche, Lisbeth', 'Roth, Andrea', 'Doyle, John (I)', 'Quartaroli, Peter', 'Hassan, Jalaluddin', 'Rogers, AnnieScott', 'Downing, Sara (I)'] Actors with maximum degree: ['Davis, Mark (V)']
Looking for the actor names Davis, Mark (V) has the highest degree, who is know for the Adult main genre and played mostly there. He is "Britain's biggest male porn star in America".
# create a list of actors with max degree
actors_with_max_degree = df_degrees.loc[df_degrees['degree'] == max_degree, 'node'].tolist()
# filter the original dataframe by actors with max degree
df_max_degree = df_with_genres[df_with_genres['id'].isin(actors_with_max_degree)]
df_max_degree
| id | ActorName | movies_95_04 | main_genre | Mystery | Adult | Fantasy | Action | Family | Romance | Comedy | Music | Crime | Horror | Animation | Thriller | Musical | NULL | Drama | Western | Adventure | Short | Documentary | War | Sci-Fi | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 12147 | n162 | Davis, Mark (V) | 540 | Adult | 0 | 429 | 1 | 1 | 0 | 0 | 3 | 0 | 1 | 0 | 0 | 0 | 0 | 92 | 6 | 0 | 0 | 1 | 5 | 0 | 1 |
The actors with one degree are mainly from the Comedy, Drama and Romance main genres. It looks like these actors were less successful than other actors in the dataset hence they only one connection.
# create a list of actors with minimum degree
actors_with_min_degree = df_degrees.loc[df_degrees['degree'] == min_degree, 'node'].tolist()
# filter the original dataframe by actors with minimum degree
df_min_degree = df_with_genres[df_with_genres['id'].isin(actors_with_min_degree)]
# show all columns
pd.set_option('display.max_rows',None)
df_min_degree
| id | ActorName | movies_95_04 | main_genre | Mystery | Adult | Fantasy | Action | Family | Romance | Comedy | Music | Crime | Horror | Animation | Thriller | Musical | NULL | Drama | Western | Adventure | Short | Documentary | War | Sci-Fi | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 20 | n9764 | Sokoloff, Marla | 11 | Comedy | 0 | 0 | 0 | 0 | 0 | 1 | 3 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 2 | 0 | 0 | 1 | 0 | 0 | 2 |
| 41 | n6151 | Bonham-Carter, Crispin | 10 | Drama | 0 | 0 | 0 | 0 | 0 | 2 | 1 | 0 | 1 | 1 | 0 | 0 | 0 | 2 | 3 | 0 | 0 | 0 | 0 | 0 | 0 |
| 68 | n17479 | Nagatsuka, Kyozo | 10 | Romance | 0 | 0 | 0 | 0 | 0 | 3 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 5 | 2 | 0 | 0 | 0 | 0 | 0 | 0 |
| 76 | n10577 | R'Mante, Adrian | 10 | Comedy | 0 | 0 | 0 | 0 | 1 | 0 | 3 | 1 | 0 | 0 | 0 | 1 | 0 | 1 | 2 | 0 | 0 | 0 | 1 | 0 | 0 |
| 114 | n11616 | Moore, Rudy Ray | 12 | Comedy | 0 | 0 | 0 | 0 | 0 | 0 | 7 | 1 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 1 |
| 138 | n14370 | Young, Dey | 16 | Thriller | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 6 | 0 | 0 | 6 | 0 | 0 | 1 | 1 | 0 | 0 |
| 146 | n15989 | Lemche, Kris | 13 | Drama | 1 | 0 | 2 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 6 | 0 | 0 | 0 | 0 | 0 | 0 |
| 163 | n1479 | Schönemann, Hinnerk | 10 | Drama | 0 | 0 | 0 | 0 | 0 | 3 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 4 | 0 | 0 | 1 | 0 | 1 | 0 |
| 189 | n17623 | Peluffo, Mariano | 12 | Family | 0 | 0 | 0 | 0 | 6 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 5 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 248 | n14631 | Ulliel, Gaspard | 14 | Drama | 1 | 0 | 0 | 0 | 0 | 2 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 5 | 2 | 0 | 0 | 1 | 0 | 1 | 0 |
| 252 | n16224 | Offerman, Nick | 12 | Drama | 0 | 0 | 0 | 0 | 0 | 3 | 3 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 4 | 0 | 0 | 2 | 0 | 0 | 0 |
| 262 | n11477 | Rieck, Billy | 10 | Thriller | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 3 | 0 | 0 | 0 | 1 | 1 | 1 | 0 | 0 | 2 |
| 312 | n17093 | Busse, John | 10 | Drama | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 4 | 1 | 0 | 1 | 2 | 0 | 0 | 0 |
| 367 | n8316 | Krusiec, Michelle | 13 | Comedy | 0 | 0 | 0 | 0 | 1 | 3 | 3 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 2 | 0 | 1 | 2 | 0 | 0 | 0 |
| 433 | n11572 | Baruc, Siri | 12 | Drama | 0 | 0 | 0 | 1 | 0 | 2 | 2 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 3 | 0 | 0 | 0 | 0 | 0 | 1 |
| 450 | n12592 | Collins, Paul (I) | 10 | Drama | 0 | 0 | 0 | 0 | 0 | 0 | 3 | 0 | 0 | 0 | 0 | 2 | 0 | 0 | 3 | 0 | 0 | 0 | 0 | 0 | 2 |
| 484 | n17250 | Peterson, Cassandra | 14 | Horror | 0 | 1 | 0 | 0 | 0 | 0 | 2 | 0 | 0 | 6 | 0 | 0 | 0 | 2 | 0 | 0 | 0 | 2 | 1 | 0 | 0 |
| 504 | n15576 | Kurtiz, Tuncel | 14 | Drama | 0 | 0 | 0 | 0 | 0 | 2 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 5 | 0 | 0 | 1 | 4 | 0 | 0 |
| 524 | n11977 | Bossa, Luly | 13 | Romance | 0 | 0 | 0 | 0 | 0 | 3 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 7 | 2 | 0 | 0 | 0 | 0 | 0 | 0 |
| 527 | n7607 | de Mylius, Jørgen | 10 | Music | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 7 | 0 | 0 |
| 646 | n7063 | Newton, Wayne | 12 | Drama | 0 | 1 | 0 | 0 | 0 | 1 | 2 | 2 | 0 | 0 | 0 | 0 | 0 | 2 | 2 | 0 | 0 | 0 | 2 | 0 | 0 |
| 683 | n15803 | Stuart, James Patrick | 10 | Comedy | 0 | 0 | 0 | 0 | 0 | 0 | 4 | 0 | 0 | 0 | 0 | 1 | 0 | 2 | 1 | 0 | 0 | 0 | 0 | 1 | 1 |
| 706 | n12596 | Julien, Jean-Luc | 12 | Romance | 0 | 0 | 2 | 0 | 0 | 3 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 2 | 2 | 1 | 0 |
| 709 | n3698 | Douglas, Shirley | 11 | Drama | 0 | 0 | 0 | 0 | 2 | 0 | 1 | 0 | 0 | 0 | 0 | 2 | 0 | 0 | 5 | 0 | 0 | 0 | 1 | 0 | 0 |
| 754 | n2028 | Michael, Christopher | 14 | Drama | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 2 | 0 | 1 | 5 | 0 | 0 | 1 | 1 | 0 | 1 |
| 755 | n3838 | Davis, Sarah Scott | 10 | Drama | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 4 | 2 | 0 | 0 | 1 | 0 | 0 | 0 |
| 760 | n8809 | Van Wormer, Steve | 10 | Comedy | 0 | 0 | 0 | 0 | 0 | 2 | 4 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 2 | 0 | 0 | 0 | 0 | 0 | 1 |
| 830 | n11452 | Dempsey, Michael (I) | 13 | Drama | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 6 | 0 | 0 | 2 | 0 | 0 | 2 |
| 871 | n7742 | Zehetner, Nora | 10 | Comedy | 1 | 0 | 0 | 0 | 0 | 0 | 3 | 0 | 0 | 0 | 0 | 1 | 0 | 2 | 2 | 0 | 0 | 0 | 0 | 1 | 0 |
| 878 | n2105 | Romeo, Marc | 10 | Drama | 0 | 0 | 0 | 0 | 0 | 2 | 0 | 0 | 1 | 1 | 0 | 1 | 0 | 0 | 3 | 0 | 0 | 2 | 0 | 0 | 0 |
| 896 | n17800 | Ravitz, Nati | 10 | Romance | 0 | 0 | 0 | 0 | 0 | 4 | 2 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 2 | 0 | 0 | 0 | 0 | 1 | 0 |
| 971 | n12164 | Jennings, Brent | 10 | Drama | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 5 | 0 | 0 | 1 | 0 | 0 | 0 |
| 1075 | n13888 | Jacot, Christopher | 10 | Drama | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 1 | 0 | 1 | 1 | 0 | 0 | 2 | 2 | 0 | 0 | 1 | 0 | 0 | 0 |
| 1142 | n14870 | Mylan, Richard | 10 | Comedy | 0 | 0 | 0 | 0 | 0 | 1 | 2 | 0 | 0 | 0 | 0 | 1 | 0 | 3 | 1 | 0 | 0 | 1 | 1 | 0 | 0 |
| 1158 | n7186 | Polk, Stephen | 12 | Action | 0 | 0 | 0 | 2 | 0 | 1 | 1 | 0 | 0 | 1 | 0 | 2 | 0 | 2 | 2 | 1 | 0 | 0 | 0 | 0 | 0 |
| 1161 | n11369 | Ryan, Thomas Jay | 13 | Comedy | 0 | 0 | 0 | 0 | 0 | 2 | 3 | 0 | 0 | 0 | 0 | 2 | 0 | 2 | 2 | 0 | 0 | 2 | 0 | 0 | 0 |
| 1206 | n17860 | Nørbygård, Finn | 11 | Comedy | 0 | 0 | 0 | 0 | 3 | 0 | 3 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 | 0 | 0 |
| 1359 | n9128 | Leroux, Maxime | 19 | Drama | 1 | 0 | 0 | 0 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 7 | 6 | 0 | 0 | 2 | 0 | 0 | 0 |
| 1373 | n6979 | Drake, David (I) | 10 | Drama | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 | 0 | 0 | 5 | 0 | 0 | 0 |
| 1401 | n4888 | Cooper, Rowena | 11 | Drama | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 6 | 0 | 1 | 0 | 0 | 0 | 0 |
| 1419 | n10942 | Blanche, Robert | 19 | Drama | 1 | 0 | 0 | 0 | 0 | 0 | 3 | 0 | 0 | 0 | 0 | 1 | 0 | 4 | 8 | 0 | 0 | 1 | 0 | 0 | 1 |
| 1427 | n17388 | Karsenti, Sabine | 10 | Drama | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 2 | 2 | 0 | 1 | 1 | 0 | 0 | 1 |
| 1478 | n10120 | Fernandez, Peter (I) | 11 | Animation | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 5 | 1 | 0 | 2 | 2 | 0 | 0 | 0 | 0 | 0 | 0 |
| 1483 | n16362 | Wu, Robert | 10 | Thriller | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 0 | 0 | 0 | 1 | 3 | 0 | 1 | 1 | 0 | 0 | 2 | 0 | 0 | 0 |
| 1495 | n17633 | Shakibai, Khosro | 12 | Drama | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 8 | 3 | 0 | 0 | 0 | 0 | 0 | 0 |
| 1570 | n13435 | Söllner, Pippi | 11 | Drama | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 6 | 2 | 0 | 0 | 1 | 0 | 0 | 0 |
| 1587 | n16661 | Bomonde, Betty | 10 | Thriller | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 7 | 0 | 0 | 1 | 0 | 0 | 0 | 0 |
| 1604 | n17915 | Polonia, John | 11 | Horror | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 3 |
| 1626 | n10967 | Hoffman, Jackie | 10 | Comedy | 0 | 0 | 0 | 0 | 1 | 3 | 3 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 1 | 0 | 0 | 0 |
| 1631 | n12244 | Werner, Roy | 12 | Drama | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 3 | 0 | 2 | 3 | 0 | 0 | 0 | 0 | 1 | 0 |
| 1652 | n14848 | Godboldo, Dale | 11 | Comedy | 0 | 0 | 0 | 1 | 0 | 0 | 5 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 2 | 0 | 0 | 0 | 0 | 0 | 1 |
| 1653 | n11592 | Gaffney, Mo | 11 | Comedy | 0 | 0 | 0 | 0 | 0 | 0 | 6 | 0 | 0 | 0 | 0 | 0 | 0 | 3 | 1 | 0 | 0 | 0 | 1 | 0 | 0 |
| 1681 | n15747 | Russom, Leon | 12 | Drama | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 4 | 4 | 0 | 0 | 1 | 0 | 0 | 1 |
| 1736 | n15782 | Blanks, Billy | 14 | Action | 0 | 0 | 0 | 2 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 2 | 0 | 7 | 1 | 0 | 0 | 0 | 1 | 0 | 0 |
| 1772 | n9182 | Limas, Jim Adhi | 12 | Romance | 1 | 0 | 0 | 0 | 0 | 3 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 3 | 1 | 0 | 0 | 1 | 0 | 0 | 1 |
| 1822 | n10569 | Schwartzman, Jason | 14 | Drama | 0 | 0 | 1 | 0 | 1 | 1 | 3 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 | 0 | 0 | 2 | 0 | 0 | 0 |
| 1824 | n7468 | Sec, Frantisek | 10 | Drama | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 7 | 2 | 0 | 0 |
| 1918 | n2802 | Needham, Tracey | 10 | Drama | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 2 | 0 | 0 | 4 | 0 | 0 | 1 | 0 | 1 | 1 |
| 1954 | n4250 | De Bankolé, Isaach | 10 | Drama | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 6 | 0 | 0 | 0 | 0 | 1 | 1 |
| 1980 | n15115 | Calzone, Maria Pia | 11 | Thriller | 0 | 0 | 0 | 0 | 0 | 1 | 2 | 0 | 0 | 0 | 0 | 2 | 0 | 3 | 1 | 0 | 0 | 1 | 0 | 1 | 0 |
| 1983 | n7937 | Vukotic, Milena | 11 | Comedy | 0 | 0 | 0 | 0 | 0 | 1 | 7 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 1 | 0 | 0 | 0 |
| 2026 | n9378 | Westfeldt, Jennifer | 10 | Romance | 1 | 0 | 0 | 0 | 0 | 2 | 2 | 1 | 0 | 0 | 0 | 0 | 0 | 2 | 1 | 0 | 0 | 0 | 1 | 0 | 0 |
| 2031 | n11606 | Williams, Lia | 11 | Drama | 0 | 0 | 0 | 0 | 0 | 1 | 2 | 1 | 0 | 0 | 0 | 0 | 0 | 2 | 4 | 0 | 0 | 0 | 0 | 1 | 0 |
| 2042 | n14444 | Bolkan, Florinda | 10 | Thriller | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 3 | 0 | 4 | 1 | 0 | 0 | 1 | 0 | 0 | 0 |
| 2060 | n4380 | von Franckenstein, Clement | 15 | Comedy | 1 | 0 | 1 | 0 | 1 | 2 | 3 | 1 | 0 | 0 | 0 | 1 | 0 | 2 | 2 | 0 | 0 | 1 | 0 | 0 | 0 |
| 2127 | n7017 | Quinn, Francesco | 12 | Thriller | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 5 | 0 | 1 | 3 | 0 | 1 | 0 | 0 | 0 | 0 |
| 2169 | n12264 | Karven, Ursula | 12 | Drama | 2 | 0 | 0 | 1 | 0 | 1 | 3 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 3 | 0 | 0 | 0 | 0 | 0 | 0 |
| 2175 | n13660 | Cordy, Annie | 13 | Music | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 7 | 0 | 0 | 0 | 3 | 1 | 0 | 0 |
| 2189 | n13327 | Dumaurier, Francis | 11 | Drama | 0 | 0 | 1 | 0 | 0 | 1 | 2 | 0 | 1 | 0 | 0 | 1 | 0 | 1 | 3 | 0 | 0 | 0 | 0 | 1 | 0 |
| 2345 | n17842 | Guskov, Aleksei | 10 | Drama | 0 | 0 | 0 | 1 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 5 | 0 | 0 | 0 | 0 | 0 | 0 |
| 2445 | n17910 | Wynn, Anthony | 11 | NaN | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 11 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 2449 | n15839 | Neale, Brent | 10 | Thriller | 0 | 0 | 1 | 0 | 0 | 0 | 2 | 0 | 0 | 1 | 0 | 2 | 0 | 1 | 1 | 0 | 0 | 2 | 0 | 0 | 0 |
| 2461 | n15744 | Holt, David (III) | 13 | Comedy | 0 | 0 | 0 | 0 | 0 | 1 | 5 | 1 | 0 | 0 | 3 | 0 | 1 | 0 | 0 | 0 | 0 | 2 | 0 | 0 | 0 |
| 2480 | n17882 | Walsh, Darren (I) | 18 | Comedy | 0 | 0 | 0 | 0 | 0 | 1 | 6 | 0 | 0 | 0 | 6 | 0 | 0 | 0 | 0 | 0 | 0 | 5 | 0 | 0 | 0 |
| 2499 | n16829 | McManus, Rove | 11 | Comedy | 0 | 0 | 0 | 0 | 0 | 0 | 4 | 2 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 3 | 0 | 0 |
| 2502 | n16888 | Sterling, Rachel | 11 | Comedy | 0 | 0 | 0 | 0 | 0 | 2 | 2 | 0 | 1 | 0 | 0 | 0 | 0 | 5 | 0 | 0 | 0 | 0 | 1 | 0 | 0 |
| 2545 | n8841 | Shimono, Sab | 11 | Thriller | 0 | 1 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 2 | 0 | 0 | 2 | 0 | 0 | 1 | 1 | 1 | 1 |
| 2631 | n11550 | Brener, Shirly | 12 | Drama | 0 | 0 | 0 | 0 | 0 | 2 | 2 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 6 | 0 | 0 | 0 | 0 | 0 | 0 |
| 2678 | n17533 | Cha, Seung-won | 10 | Drama | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 5 | 0 | 0 | 0 | 0 | 0 | 0 |
| 2768 | n17447 | Yankovsky, Oleg | 10 | Drama | 0 | 0 | 0 | 0 | 0 | 3 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 4 | 0 | 0 | 0 | 0 | 0 | 1 |
| 2802 | n16072 | Azizi, Anthony | 11 | Comedy | 1 | 0 | 0 | 0 | 0 | 1 | 2 | 0 | 0 | 1 | 0 | 1 | 0 | 4 | 1 | 0 | 0 | 0 | 0 | 0 | 0 |
| 2838 | n4049 | Fox, Emilia | 18 | Drama | 2 | 0 | 0 | 0 | 0 | 2 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 6 | 0 | 0 | 3 | 1 | 1 | 0 |
| 2882 | n8421 | Landry, Ali | 14 | Action | 0 | 1 | 0 | 2 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 5 | 1 | 0 | 0 | 1 | 2 | 0 | 1 |
| 2923 | n8454 | Robinson, Andrew (I) | 10 | Sci-Fi | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 3 | 0 | 0 | 0 | 0 | 3 | 0 | 1 |
| 2979 | n5718 | Anthony, Lysette | 16 | Thriller | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 2 | 0 | 7 | 0 | 0 | 4 | 0 | 0 | 0 | 1 | 0 | 1 |
| 2980 | n10394 | Canals, Maria | 10 | Comedy | 0 | 0 | 1 | 0 | 1 | 1 | 3 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 2 | 0 | 1 | 0 | 0 | 0 | 0 |
| 3000 | n4976 | Kober, Jeff | 18 | Drama | 1 | 0 | 0 | 2 | 0 | 1 | 1 | 0 | 1 | 0 | 0 | 2 | 0 | 2 | 5 | 0 | 1 | 0 | 1 | 0 | 1 |
| 3039 | n10691 | Babcock, Todd | 16 | Romance | 1 | 0 | 2 | 1 | 0 | 2 | 1 | 0 | 1 | 1 | 0 | 1 | 0 | 2 | 1 | 0 | 0 | 3 | 0 | 0 | 0 |
| 3049 | n6746 | Kerman, Ken | 10 | Drama | 1 | 0 | 0 | 0 | 0 | 2 | 1 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 3 | 0 | 0 | 1 | 0 | 0 | 0 |
| 3065 | n6587 | Krauss, Naomi | 13 | Crime | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 7 | 0 | 0 | 0 | 0 | 0 | 4 | 0 | 0 | 0 | 1 | 0 | 0 |
| 3120 | n17477 | Tokiwa, Takako | 11 | Romance | 0 | 0 | 1 | 0 | 0 | 4 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 4 | 1 | 0 | 0 | 0 | 0 | 0 | 0 |
| 3156 | n15221 | Thomas, Naím | 11 | Music | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 2 | 0 | 0 | 0 | 1 | 1 | 0 | 1 | 1 | 0 | 2 | 0 | 1 | 0 |
| 3165 | n13904 | Mills, Judson | 12 | Thriller | 0 | 0 | 0 | 1 | 0 | 1 | 1 | 0 | 0 | 1 | 0 | 3 | 0 | 1 | 2 | 0 | 0 | 0 | 0 | 1 | 1 |
| 3167 | n17242 | Tjalsma, Joke | 10 | Drama | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 6 | 3 | 0 | 0 | 0 | 0 | 0 | 0 |
| 3248 | n4082 | McMurtry, Michael | 10 | Drama | 0 | 0 | 0 | 0 | 0 | 2 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 4 | 0 | 0 | 2 | 0 | 0 | 0 |
| 3249 | n15810 | Kirk, Justin | 11 | Comedy | 0 | 0 | 0 | 0 | 0 | 3 | 3 | 0 | 0 | 0 | 0 | 2 | 0 | 0 | 2 | 0 | 0 | 1 | 0 | 0 | 0 |
| 3267 | n6367 | Agenin, Béatrice | 13 | Drama | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 7 | 1 | 0 | 0 | 2 | 0 | 0 | 0 |
| 3269 | n17807 | Levy, Nir (I) | 10 | Drama | 0 | 0 | 0 | 0 | 0 | 2 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 | 0 | 0 | 0 | 0 | 0 | 0 |
| 3288 | n13653 | Sola, Catherine | 10 | Drama | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 4 | 4 | 0 | 0 | 0 | 0 | 0 | 0 |
| 3297 | n7395 | Rogan, Joe | 10 | Comedy | 0 | 0 | 0 | 1 | 1 | 0 | 4 | 0 | 0 | 0 | 0 | 0 | 0 | 3 | 0 | 0 | 1 | 0 | 0 | 0 | 0 |
| 3312 | n9301 | Strozier, Henry | 13 | Comedy | 0 | 0 | 0 | 1 | 0 | 0 | 3 | 1 | 0 | 0 | 0 | 1 | 0 | 3 | 1 | 0 | 0 | 0 | 2 | 1 | 0 |
| 3350 | n10677 | Bennett, Fran (I) | 10 | Drama | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 3 | 0 | 1 | 2 | 1 | 0 | 0 |
| 3457 | n16492 | Garner, Kelli | 11 | Drama | 0 | 0 | 0 | 0 | 0 | 1 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 4 | 0 | 0 | 3 | 0 | 0 | 0 |
| 3485 | n14930 | Carroll, Justin | 12 | Drama | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 2 | 0 | 2 | 0 | 2 | 2 | 0 | 0 | 1 | 0 | 0 | 1 |
| 3518 | n17406 | Alvarez, Juan Luis | 18 | Musical | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 2 | 1 | 0 | 0 | 3 | 11 | 0 | 0 |
| 3527 | n11855 | Wildbolz, Klaus | 10 | Drama | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 7 | 2 | 0 | 0 | 0 | 0 | 0 | 0 |
| 3568 | n13498 | Fallenstein, Karina | 10 | Drama | 0 | 0 | 0 | 0 | 0 | 2 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 3 | 4 | 0 | 0 | 0 | 0 | 0 | 0 |
| 3570 | n11424 | Walker, Polly (II) | 12 | Drama | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 1 | 1 | 0 | 1 | 0 | 0 | 6 | 0 | 0 | 0 | 0 | 1 | 0 |
| 3595 | n17708 | Mims, Roxzane T. | 10 | Drama | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 4 | 2 | 0 | 0 | 1 | 0 | 0 | 1 |
| 3602 | n7439 | Brady, Orla | 12 | Drama | 0 | 0 | 0 | 0 | 0 | 2 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 7 | 0 | 1 | 0 | 0 | 0 | 0 |
| 3606 | n14262 | Maddox, Billy | 16 | Sci-Fi | 0 | 0 | 0 | 0 | 2 | 0 | 1 | 0 | 0 | 0 | 0 | 2 | 0 | 5 | 1 | 0 | 0 | 2 | 0 | 0 | 3 |
| 3613 | n13008 | Damien (III) | 11 | Adult | 0 | 9 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 3635 | n11858 | Ullmann, Kostja | 12 | Romance | 0 | 0 | 0 | 0 | 0 | 3 | 1 | 0 | 2 | 0 | 0 | 0 | 0 | 3 | 2 | 0 | 0 | 1 | 0 | 0 | 0 |
| 3717 | n10869 | Lawrence, Sharon | 16 | Drama | 0 | 0 | 0 | 0 | 0 | 2 | 2 | 0 | 0 | 1 | 0 | 3 | 0 | 3 | 3 | 0 | 0 | 1 | 1 | 0 | 0 |
| 3736 | n15586 | Gibbons, Leeza | 10 | Family | 0 | 0 | 0 | 0 | 2 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 2 | 2 | 0 | 0 | 0 | 3 | 0 | 0 |
| 3815 | n15121 | Lawson, Denis | 13 | Drama | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 2 | 4 | 0 | 1 | 0 | 2 | 0 | 0 |
| 3817 | n2818 | Craig, Andrew (I) | 10 | Adventure | 0 | 0 | 1 | 0 | 1 | 1 | 0 | 0 | 1 | 0 | 0 | 2 | 0 | 0 | 2 | 0 | 2 | 0 | 0 | 0 | 0 |
| 3886 | n11364 | Chamberlin, Kevin | 11 | Drama | 0 | 1 | 0 | 0 | 1 | 1 | 0 | 0 | 1 | 0 | 0 | 2 | 0 | 0 | 4 | 0 | 0 | 0 | 0 | 1 | 0 |
| 3910 | n16120 | Matsushita, Yuki | 11 | Drama | 0 | 0 | 0 | 0 | 1 | 0 | 3 | 0 | 0 | 0 | 0 | 0 | 0 | 4 | 3 | 0 | 0 | 0 | 0 | 0 | 0 |
| 3919 | n17295 | Bubber | 12 | Family | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 4 | 0 | 0 | 0 | 1 | 6 | 0 | 0 |
| 3997 | n7793 | Stover, George | 11 | Sci-Fi | 2 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 2 | 0 | 1 | 0 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 3 |
| 4053 | n15667 | Johnson, Rick (I) | 10 | Thriller | 0 | 0 | 0 | 0 | 0 | 2 | 1 | 0 | 0 | 0 | 0 | 2 | 0 | 1 | 2 | 0 | 0 | 1 | 0 | 0 | 1 |
| 4093 | n10277 | Pickles, Carolyn | 10 | Drama | 0 | 0 | 1 | 0 | 0 | 0 | 3 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 4 | 0 | 0 | 0 | 0 | 0 | 0 |
| 4111 | n13082 | Cannatella, Trishelle | 11 | Comedy | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 1 | 0 | 7 | 0 | 0 |
| 4150 | n13924 | Franek, Ivan | 10 | Drama | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 2 | 5 | 0 | 1 | 0 | 0 | 0 | 0 |
| 4166 | n7438 | Petrucci, Luigi (I) | 10 | Comedy | 0 | 0 | 0 | 0 | 0 | 2 | 2 | 0 | 0 | 0 | 0 | 1 | 0 | 3 | 2 | 0 | 0 | 0 | 0 | 0 | 0 |
| 4174 | n2009 | Cutzarida, Ivo | 13 | Drama | 0 | 0 | 0 | 1 | 0 | 1 | 2 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 6 | 0 | 0 | 1 | 0 | 0 | 0 |
| 4242 | n13790 | Moritz, Dorothea | 10 | Thriller | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 2 | 0 | 4 | 1 | 0 | 0 | 1 | 0 | 1 | 0 |
| 4247 | n9922 | Caparrós, Alonso | 12 | Musical | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 2 | 6 | 2 | 0 | 1 | 0 | 0 | 0 | 0 |
| 4284 | n16376 | Riemelt, Max | 11 | Comedy | 0 | 0 | 0 | 0 | 1 | 0 | 3 | 0 | 0 | 0 | 0 | 1 | 0 | 2 | 2 | 0 | 1 | 0 | 0 | 1 | 0 |
| 4293 | n10150 | Yenque, Jose | 10 | Drama | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 2 | 0 | 1 | 5 | 0 | 0 | 1 | 0 | 0 | 0 |
| 4307 | n11006 | Ellis, Aunjanue | 16 | Drama | 0 | 0 | 0 | 0 | 0 | 1 | 3 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 9 | 0 | 0 | 2 | 0 | 0 | 0 |
| 4335 | n1229 | Kae-Kazim, Hakeem | 10 | Drama | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 5 | 0 | 1 | 0 | 0 | 1 | 0 |
| 4343 | n10824 | Buendía, Rafael | 20 | Action | 0 | 0 | 0 | 13 | 0 | 0 | 4 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 2 | 0 | 0 | 0 | 0 | 0 | 0 |
| 4427 | n393 | Langton, Brooke | 12 | Drama | 0 | 0 | 0 | 0 | 1 | 1 | 2 | 0 | 0 | 0 | 0 | 3 | 0 | 0 | 4 | 0 | 0 | 0 | 1 | 0 | 0 |
| 4463 | n16799 | Mailhouse, Robert | 10 | Drama | 1 | 0 | 0 | 0 | 0 | 3 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 3 | 0 | 0 | 0 | 1 | 0 | 0 |
| 4510 | n15916 | Desverchère, Jocelyne | 10 | Romance | 0 | 0 | 0 | 0 | 0 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 2 | 0 | 0 | 5 | 0 | 0 | 0 |
| 4735 | n16369 | Barry, Rod | 12 | Adult | 0 | 4 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 5 | 0 | 0 | 0 | 1 | 1 | 0 | 0 |
| 4757 | n7575 | Linden, Hal | 10 | Thriller | 1 | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 0 | 0 | 0 | 3 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 1 |
| 4773 | n14745 | Deret, Jean-Claude | 10 | Comedy | 0 | 0 | 0 | 0 | 1 | 1 | 2 | 0 | 0 | 1 | 0 | 1 | 0 | 2 | 0 | 0 | 0 | 1 | 1 | 0 | 0 |
| 4789 | n9516 | Berman, Andy | 11 | Romance | 0 | 0 | 0 | 0 | 0 | 3 | 3 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 2 | 0 | 0 | 0 | 0 | 0 | 1 |
| 4831 | n16250 | Abbas, Hiyam | 13 | Drama | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 | 6 | 0 | 0 | 3 | 0 | 1 | 0 |
| 4996 | n883 | Alexander, Flex | 14 | Drama | 1 | 0 | 1 | 0 | 0 | 2 | 2 | 0 | 0 | 1 | 0 | 2 | 0 | 1 | 3 | 0 | 0 | 0 | 0 | 0 | 1 |
| 5075 | n4892 | Reid, Mike (I) | 10 | Comedy | 0 | 0 | 0 | 0 | 0 | 0 | 5 | 0 | 0 | 0 | 0 | 2 | 0 | 0 | 1 | 0 | 0 | 0 | 2 | 0 | 0 |
| 5081 | n17410 | Tamura, Eriko | 16 | Drama | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 11 | 2 | 0 | 0 | 0 | 0 | 1 | 0 |
| 5104 | n13885 | Caldwell, L. Scott | 11 | Drama | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 2 | 0 | 2 | 4 | 0 | 0 | 0 | 0 | 0 | 1 |
| 5175 | n11421 | Loughlin, Lori | 13 | Drama | 1 | 0 | 1 | 0 | 0 | 0 | 2 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 7 | 0 | 0 | 0 | 0 | 0 | 0 |
| 5219 | n5530 | Brennan, Eileen | 10 | Drama | 0 | 0 | 1 | 0 | 0 | 0 | 3 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 4 | 0 | 0 | 1 | 0 | 0 | 0 |
| 5244 | n6383 | Parfitt, Judy | 12 | Drama | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 3 | 0 | 0 | 6 | 0 | 0 | 0 | 0 | 1 | 0 |
| 5284 | n6902 | Hipp, Paul (I) | 10 | Drama | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 1 | 0 | 1 | 0 | 1 | 4 | 0 | 0 | 0 | 0 | 0 | 1 |
| 5330 | n8118 | Mari, Gina | 10 | Drama | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 2 | 0 | 1 | 2 | 0 | 0 | 1 | 1 | 0 | 1 |
| 5341 | n15316 | Miller, Kristen | 10 | Comedy | 1 | 0 | 1 | 0 | 0 | 0 | 4 | 0 | 0 | 1 | 0 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 |
| 5383 | n3640 | Coogan, Keith | 10 | Drama | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 2 | 0 | 1 | 4 | 0 | 0 | 1 | 0 | 0 | 0 |
| 5441 | n12123 | Lyles, Leslie | 12 | Drama | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 3 | 4 | 0 | 0 | 2 | 0 | 0 | 0 |
| 5505 | n17859 | Soriat, Bettina | 10 | Music | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 6 | 0 | 0 | 0 | 0 | 0 | 3 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 5527 | n8842 | Flaherty, Lanny | 10 | Drama | 1 | 1 | 1 | 0 | 1 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 4 | 0 | 0 | 0 | 0 | 0 | 0 |
| 5577 | n15479 | Rosen, Beatrice | 12 | Comedy | 0 | 0 | 0 | 0 | 1 | 1 | 3 | 0 | 0 | 0 | 0 | 1 | 0 | 4 | 1 | 0 | 0 | 1 | 0 | 0 | 0 |
| 5636 | n3011 | Havers, Nigel | 13 | Drama | 0 | 0 | 0 | 0 | 0 | 1 | 2 | 1 | 0 | 1 | 0 | 1 | 0 | 1 | 4 | 0 | 0 | 0 | 2 | 0 | 0 |
| 5651 | n10167 | Kiely, Mark | 10 | Thriller | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 2 | 0 | 1 | 1 | 0 | 0 | 2 | 0 | 0 | 2 |
| 5714 | n3861 | Kraljevic, Ivan | 11 | Action | 1 | 0 | 0 | 2 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 1 | 0 | 1 | 0 | 1 | 0 | 0 |
| 5730 | n15953 | Baker, George (I) | 10 | Fantasy | 1 | 1 | 2 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 2 | 0 | 0 |
| 5857 | n12156 | Rodríguez, Marco (I) | 10 | Comedy | 1 | 1 | 0 | 2 | 0 | 0 | 2 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 2 | 1 | 0 | 0 | 0 | 0 | 0 |
| 5879 | n2048 | Gossett, Robert | 11 | Drama | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 0 | 0 | 5 | 0 | 0 | 1 | 0 | 0 | 1 |
| 5909 | n12909 | Marchelletta, Jeff | 11 | Thriller | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 3 | 0 | 4 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 |
| 5957 | n2131 | Jordan, Leslie | 10 | Comedy | 0 | 0 | 0 | 0 | 0 | 0 | 4 | 0 | 0 | 0 | 0 | 1 | 0 | 2 | 2 | 0 | 0 | 1 | 0 | 0 | 0 |
| 5998 | n3797 | Jones, January (I) | 10 | Drama | 0 | 0 | 0 | 0 | 0 | 3 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 5 | 0 | 0 | 0 | 0 | 0 | 0 |
| 6010 | n7292 | Mitchum, Christopher | 10 | Thriller | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 2 | 0 | 4 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
| 6037 | n8050 | Elias, Cyrus | 10 | Thriller | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 1 | 1 | 0 | 2 | 0 | 1 | 1 | 0 | 0 | 2 | 0 | 0 | 0 |
| 6050 | n3822 | Howerton, Charles | 10 | Comedy | 0 | 0 | 0 | 0 | 0 | 0 | 3 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 1 | 1 | 0 | 2 |
| 6113 | n16606 | Brady, Moya | 10 | Drama | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 2 | 0 | 3 | 2 | 0 | 0 | 2 | 0 | 0 | 0 |
| 6142 | n2823 | Dent, Cheryl | 15 | Thriller | 1 | 0 | 0 | 0 | 0 | 0 | 2 | 0 | 1 | 1 | 0 | 3 | 0 | 5 | 0 | 0 | 0 | 0 | 1 | 0 | 1 |
| 6165 | n16766 | Rivière, Marie | 11 | Drama | 0 | 0 | 0 | 0 | 0 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 6 | 0 | 0 | 2 | 0 | 0 | 0 |
| 6225 | n14123 | Coulson, Lindsey | 13 | Drama | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 9 | 0 | 0 | 0 | 1 | 0 | 0 |
| 6281 | n17903 | Lähde, Ville (I) | 10 | Horror | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 0 | 0 | 3 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 2 | 0 | 0 | 1 |
| 6282 | n17901 | Alexander, Sharon (I) | 11 | Drama | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 8 | 0 | 0 | 1 | 0 | 0 | 0 |
| 6287 | n13828 | Hayes, David C. | 16 | Horror | 0 | 0 | 0 | 0 | 0 | 0 | 3 | 0 | 0 | 4 | 0 | 0 | 0 | 3 | 1 | 1 | 0 | 4 | 0 | 0 | 0 |
| 6301 | n17076 | Perine, Kelly | 12 | Comedy | 0 | 0 | 0 | 0 | 0 | 1 | 5 | 0 | 0 | 1 | 0 | 2 | 0 | 1 | 2 | 0 | 0 | 0 | 0 | 0 | 0 |
| 6367 | n17822 | Lee, Mark (X) | 10 | Comedy | 0 | 0 | 0 | 0 | 1 | 1 | 4 | 0 | 2 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 |
| 6385 | n6396 | Langham, Chris | 10 | Comedy | 0 | 0 | 1 | 0 | 1 | 0 | 4 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 2 | 0 | 0 |
| 6418 | n13967 | Palmer-Stoll, Julia | 14 | Crime | 0 | 0 | 0 | 0 | 2 | 1 | 1 | 0 | 4 | 0 | 0 | 3 | 0 | 1 | 2 | 0 | 0 | 0 | 0 | 0 | 0 |
| 6481 | n5586 | Marshall, Paula | 11 | Comedy | 0 | 0 | 0 | 0 | 0 | 2 | 4 | 0 | 0 | 0 | 0 | 2 | 0 | 0 | 2 | 0 | 0 | 0 | 0 | 0 | 1 |
| 6520 | n15819 | O'Rourke, Shaun (II) | 11 | Drama | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 0 | 2 | 2 | 0 | 0 | 2 | 0 | 1 | 0 |
| 6522 | n12223 | Olds, Gabriel | 12 | Drama | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 2 | 0 | 0 | 5 | 0 | 0 | 0 | 1 | 1 | 1 |
| 6585 | n14945 | Paxton, Sara | 14 | Comedy | 1 | 0 | 2 | 0 | 3 | 1 | 4 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 |
| 6619 | n5664 | Theirse, Darryl | 12 | Comedy | 0 | 0 | 0 | 0 | 0 | 2 | 3 | 0 | 2 | 0 | 0 | 2 | 0 | 0 | 2 | 0 | 0 | 0 | 0 | 0 | 1 |
| 6664 | n2057 | Eckhouse, James | 10 | Drama | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 3 | 0 | 0 | 0 | 1 | 0 | 0 |
| 6708 | n17746 | Canton, Joanna | 10 | Drama | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 2 | 3 | 0 | 0 | 1 | 0 | 0 | 1 |
| 6710 | n17855 | Zaki, Mona | 16 | Romance | 0 | 0 | 1 | 0 | 0 | 5 | 3 | 0 | 0 | 0 | 0 | 1 | 0 | 4 | 2 | 0 | 0 | 0 | 0 | 0 | 0 |
| 6729 | n14440 | Abecassis, Yaël | 10 | Drama | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 8 | 0 | 0 | 0 | 0 | 0 | 0 |
| 6732 | n8100 | Wildman, Valerie | 11 | Drama | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 4 | 1 | 0 | 0 | 1 | 0 | 2 |
| 6735 | n17848 | Moutsatsou, Katerina | 12 | Romance | 0 | 0 | 0 | 0 | 0 | 4 | 2 | 0 | 1 | 0 | 0 | 1 | 0 | 1 | 3 | 0 | 0 | 0 | 0 | 0 | 0 |
| 6743 | n4645 | Hailer, April | 13 | Comedy | 0 | 0 | 0 | 0 | 0 | 1 | 5 | 0 | 3 | 0 | 0 | 2 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
| 6771 | n16760 | Richardson, Sy | 11 | Drama | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 2 | 3 | 0 | 0 | 0 | 2 | 0 | 1 |
| 6817 | n16367 | Wirth, Billy | 11 | Sci-Fi | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 3 | 0 | 0 | 0 | 0 | 0 | 3 |
| 6818 | n17771 | Koklas, Kostas | 12 | Comedy | 0 | 0 | 0 | 0 | 0 | 3 | 4 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 2 | 0 | 0 | 1 | 0 | 0 | 0 |
| 6819 | n13049 | Skye, Ione | 16 | Drama | 0 | 0 | 0 | 0 | 0 | 2 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 10 | 0 | 0 | 0 | 1 | 0 | 0 |
| 6834 | n1486 | Heyl, Burkhard | 10 | Drama | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 2 | 4 | 0 | 0 | 0 | 0 | 1 | 0 |
| 6914 | n2130 | Borlenghi, Matt | 10 | Thriller | 0 | 0 | 0 | 0 | 0 | 0 | 3 | 0 | 1 | 0 | 0 | 4 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
| 7012 | n17514 | Amandla | 14 | Adult | 0 | 7 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 5 | 0 | 0 | 0 | 0 | 1 | 0 | 0 |
| 7066 | n17425 | Malmberg, Claes | 10 | Comedy | 0 | 0 | 0 | 0 | 3 | 0 | 3 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 2 | 0 | 0 | 0 |
| 7108 | n8866 | Martin, Rémi | 13 | Drama | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 4 | 3 | 0 | 0 | 3 | 0 | 0 | 0 |
| 7159 | n17841 | Anderson, Fred (III) | 12 | Thriller | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 2 | 1 | 3 | 0 | 0 | 0 | 0 | 0 | 2 | 1 | 0 | 1 |
| 7245 | n14250 | Canovas, Anne | 10 | Drama | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 5 | 2 | 0 | 0 | 1 | 0 | 0 | 0 |
| 7258 | n7792 | Chamish, Leanna | 11 | Horror | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 | 0 | 1 | 0 | 0 | 1 | 0 | 1 | 1 | 3 | 0 | 1 |
| 7279 | n15273 | Gilliard, Carl | 11 | Comedy | 0 | 0 | 0 | 0 | 0 | 1 | 4 | 0 | 0 | 0 | 0 | 1 | 0 | 2 | 3 | 0 | 0 | 0 | 0 | 0 | 0 |
| 7340 | n5084 | Bémol, Brigitte | 12 | Thriller | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 2 | 0 | 6 | 1 | 0 | 0 | 1 | 0 | 0 | 0 |
| 7367 | n13853 | Azurdia, Richard | 14 | Comedy | 0 | 0 | 0 | 2 | 0 | 0 | 2 | 0 | 0 | 2 | 0 | 1 | 0 | 2 | 0 | 0 | 1 | 4 | 0 | 0 | 0 |
| 7442 | n8902 | Andrei, Lydia | 11 | Thriller | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 3 | 0 | 5 | 2 | 0 | 0 | 0 | 0 | 0 | 0 |
| 7474 | n16149 | Murray, Duane | 10 | Sci-Fi | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 2 | 2 | 0 | 0 | 1 | 0 | 0 | 2 |
| 7484 | n9297 | Romanov, Stephanie | 10 | Thriller | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 3 | 1 | 0 | 0 | 2 | 1 | 1 | 0 |
| 7515 | n16668 | Perkins, Jack (III) | 94 | NaN | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 24 | 0 | 0 | 0 | 0 | 70 | 0 | 0 |
| 7516 | n6989 | Oxenberg, Catherine | 11 | Thriller | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 7 | 0 | 1 | 2 | 0 | 1 | 0 | 0 | 0 | 0 |
| 7554 | n17852 | Shalabi, Menna | 10 | Drama | 0 | 0 | 0 | 0 | 0 | 2 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 4 | 3 | 0 | 0 | 0 | 0 | 0 | 0 |
| 7571 | n14944 | Pilato, Joseph | 10 | Horror | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 1 | 2 | 0 | 1 | 0 | 2 | 1 | 0 | 0 | 0 | 0 | 0 | 1 |
| 7572 | n7594 | Salsedo, Frank | 10 | Comedy | 0 | 0 | 1 | 1 | 0 | 0 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 1 | 1 | 1 | 1 | 0 | 0 | 0 |
| 7721 | n8092 | Malco, Romany | 12 | Comedy | 0 | 0 | 0 | 0 | 0 | 0 | 4 | 1 | 0 | 0 | 0 | 2 | 0 | 1 | 3 | 0 | 0 | 0 | 0 | 1 | 0 |
| 7765 | n3588 | Gould, Geoffrey | 10 | Drama | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 2 | 3 | 0 | 1 | 0 | 0 | 0 | 1 |
| 7769 | n8344 | Chin, Tsai (I) | 12 | Thriller | 0 | 0 | 0 | 0 | 0 | 2 | 0 | 0 | 0 | 0 | 1 | 3 | 0 | 1 | 3 | 0 | 1 | 1 | 0 | 0 | 0 |
| 7781 | n9176 | Deshors, Erick | 11 | Drama | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 1 | 0 | 7 | 1 | 0 | 0 | 0 | 0 | 0 | 0 |
| 7808 | n16360 | Livingston, Richard | 10 | Crime | 0 | 0 | 0 | 1 | 0 | 1 | 1 | 0 | 2 | 0 | 0 | 1 | 0 | 2 | 0 | 0 | 0 | 2 | 0 | 0 | 0 |
| 7887 | n17395 | Hill, Nicholas (I) | 10 | Action | 0 | 0 | 0 | 4 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 3 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 |
| 7893 | n7016 | Taylor, Mark L. | 12 | Comedy | 0 | 0 | 1 | 0 | 1 | 0 | 4 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 2 | 0 | 1 | 1 | 0 | 0 | 0 |
| 7926 | n9298 | Makinen, Karl | 11 | Drama | 0 | 0 | 0 | 0 | 0 | 2 | 2 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 4 | 0 | 0 | 0 | 0 | 1 | 0 |
| 7932 | n17575 | Dawes, Bill | 10 | Drama | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 2 | 0 | 0 | 1 | 0 | 0 | 0 |
| 7987 | n10975 | Ritter, Jason | 10 | Drama | 0 | 0 | 2 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 3 | 2 | 0 | 0 | 1 | 0 | 0 | 0 |
| 8103 | n15816 | Sky, Jennifer | 10 | Drama | 1 | 0 | 1 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 3 | 0 | 0 | 0 | 0 | 0 | 1 |
| 8113 | n469 | Strickland, KaDee | 10 | Drama | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 2 | 0 | 0 | 7 | 0 | 0 | 0 | 0 | 0 | 0 |
| 8182 | n13368 | Holmes, Teck | 11 | Comedy | 0 | 0 | 0 | 0 | 0 | 1 | 2 | 1 | 0 | 0 | 0 | 0 | 0 | 4 | 2 | 0 | 1 | 0 | 0 | 0 | 0 |
| 8218 | n10108 | Katona, Kerry | 11 | Music | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 | 0 | 0 | 0 | 1 | 0 | 1 | 1 | 0 | 0 | 0 | 5 | 0 | 0 |
| 8359 | n12385 | Quinton, Sophie | 10 | Drama | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 5 | 0 | 0 | 3 | 0 | 0 | 0 |
| 8463 | n15186 | Bremmer, Richard | 12 | Fantasy | 0 | 0 | 3 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 2 | 0 | 1 | 3 | 0 | 0 | 1 | 0 | 1 | 0 |
| 8499 | n13125 | Tortosa, Silvia | 11 | Drama | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 3 | 0 | 1 | 3 | 0 | 0 | 0 |
| 8500 | n6380 | Donovan, Daisy | 10 | Music | 1 | 0 | 0 | 0 | 0 | 0 | 2 | 2 | 0 | 0 | 0 | 0 | 0 | 3 | 1 | 0 | 0 | 0 | 0 | 0 | 1 |
| 8542 | n214 | Bergé, Francine | 13 | Drama | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 0 | 0 | 0 | 0 | 2 | 0 | 1 | 5 | 0 | 0 | 3 | 0 | 0 | 0 |
| 8624 | n12303 | Botone, Talia | 10 | Horror | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 6 | 1 | 0 | 0 | 0 | 0 | 0 | 0 |
| 8648 | n3151 | Tigar, Kenneth | 11 | Drama | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 4 | 3 | 0 | 1 | 0 | 0 | 0 | 0 |
| 8716 | n2821 | McCoy, Andre | 11 | Crime | 0 | 0 | 0 | 2 | 0 | 0 | 2 | 0 | 4 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 |
| 8767 | n14866 | Macaninch, Cal | 14 | Drama | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 0 | 2 | 6 | 0 | 0 | 2 | 0 | 0 | 0 |
| 8822 | n8926 | Lecas, Jean-Claude | 11 | Drama | 0 | 0 | 0 | 0 | 0 | 2 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 2 | 2 | 0 | 0 | 3 | 0 | 0 | 0 |
| 8904 | n13292 | Lee, Eun-ju (II) | 10 | Romance | 0 | 0 | 0 | 0 | 0 | 3 | 0 | 0 | 0 | 1 | 0 | 2 | 0 | 2 | 2 | 0 | 0 | 0 | 0 | 0 | 0 |
| 8920 | n1461 | Shaw, Vinessa | 11 | Drama | 1 | 0 | 0 | 0 | 0 | 2 | 1 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 4 | 0 | 1 | 0 | 0 | 0 | 0 |
| 8956 | n2065 | Shonka, J. Scott | 11 | War | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 3 | 0 | 0 | 1 | 0 | 0 | 1 | 1 | 3 | 0 |
| 9030 | n13802 | Hutcherson, Josh | 11 | Drama | 0 | 0 | 0 | 0 | 1 | 1 | 2 | 0 | 0 | 0 | 2 | 0 | 0 | 1 | 3 | 0 | 1 | 0 | 0 | 0 | 0 |
| 9087 | n16943 | Mennegand, Élodie | 12 | Drama | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 4 | 2 | 0 | 0 | 4 | 0 | 0 | 0 |
| 9094 | n5982 | Jade, Claude | 14 | Thriller | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 0 | 4 | 2 | 0 | 0 | 2 | 2 | 0 | 0 |
| 9182 | n10319 | Zibetti, Roberto | 10 | Drama | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 2 | 5 | 0 | 0 | 0 | 0 | 1 | 0 |
| 9214 | n17008 | Colvin, Shawn | 10 | Music | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 9 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 9289 | n10246 | Gosling, Ryan (I) | 13 | Drama | 0 | 0 | 1 | 0 | 0 | 2 | 2 | 0 | 1 | 0 | 0 | 1 | 0 | 1 | 4 | 0 | 1 | 0 | 0 | 0 | 0 |
| 9306 | n16412 | Marewski, Armin | 10 | Crime | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 2 | 0 | 0 | 2 | 0 | 1 | 1 | 0 | 0 | 2 | 0 | 1 | 0 |
| 9330 | n10976 | McCallany, Holt | 16 | Drama | 1 | 0 | 0 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 0 | 0 | 10 | 1 | 0 | 0 | 0 | 0 | 0 |
| 9409 | n12162 | Bergere, Jenica | 14 | Comedy | 0 | 0 | 0 | 0 | 0 | 1 | 5 | 0 | 0 | 1 | 0 | 2 | 0 | 2 | 2 | 0 | 1 | 0 | 0 | 0 | 0 |
| 9417 | n11262 | McKinnon, Megan | 10 | Drama | 0 | 0 | 1 | 0 | 0 | 2 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 0 | 0 | 4 | 0 | 0 | 0 |
| 9434 | n9958 | Manilow, Barry | 10 | Music | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 6 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 2 | 0 | 0 |
| 9486 | n17139 | Cayo, Fernando | 10 | Comedy | 0 | 0 | 0 | 0 | 1 | 0 | 3 | 0 | 0 | 0 | 0 | 1 | 0 | 2 | 2 | 0 | 0 | 1 | 0 | 0 | 0 |
| 9511 | n12781 | Kouyaté, Sotigui | 17 | Drama | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 2 | 0 | 8 | 5 | 0 | 0 | 1 | 0 | 0 | 0 |
| 9530 | n1462 | Henshaw, Lee | 12 | Adult | 1 | 4 | 0 | 0 | 0 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 5 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 9664 | n15008 | Booth, Emily | 18 | Comedy | 0 | 0 | 0 | 0 | 0 | 0 | 3 | 1 | 0 | 1 | 0 | 1 | 0 | 7 | 1 | 0 | 0 | 2 | 1 | 0 | 1 |
| 9687 | n11951 | Sciò, Yvonne | 13 | Comedy | 0 | 1 | 0 | 1 | 0 | 0 | 3 | 0 | 0 | 0 | 0 | 3 | 0 | 1 | 1 | 0 | 0 | 1 | 2 | 0 | 0 |
| 9693 | n16805 | De Neck, Didier | 11 | Drama | 0 | 0 | 0 | 0 | 0 | 1 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 3 | 3 | 0 | 0 | 1 | 0 | 1 | 0 |
| 9732 | n13394 | Moffett, D.W. | 14 | Drama | 0 | 0 | 0 | 0 | 0 | 1 | 3 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 9 | 0 | 0 | 1 | 0 | 0 | 0 |
| 9756 | n9159 | Shimizu, Tsuyu | 12 | Drama | 1 | 0 | 0 | 1 | 0 | 2 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 4 | 2 | 0 | 0 | 1 | 0 | 0 | 0 |
| 9810 | n2808 | Sarabia, Ric | 11 | Thriller | 0 | 0 | 1 | 1 | 0 | 0 | 1 | 0 | 2 | 1 | 0 | 5 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 9813 | n17911 | Bang, Eun-jin | 11 | Drama | 0 | 0 | 0 | 0 | 0 | 2 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 6 | 0 | 0 | 0 | 0 | 0 | 1 |
| 9870 | n5793 | Watson, Tom (I) | 10 | Drama | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 4 | 5 | 0 | 0 | 0 | 0 | 0 | 0 |
| 9875 | n17409 | Rhys, Ieuan | 10 | Drama | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 4 | 4 | 0 | 0 | 0 | 0 | 0 | 0 |
| 9910 | n8346 | Rasche, David | 15 | Comedy | 0 | 0 | 1 | 0 | 1 | 1 | 4 | 0 | 1 | 0 | 0 | 0 | 0 | 2 | 4 | 0 | 0 | 1 | 0 | 0 | 0 |
| 9925 | n7355 | Mealing, Amanda | 10 | Drama | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 7 | 0 | 0 | 0 | 0 | 0 | 0 |
| 9930 | n11091 | Howard, Ken (I) | 13 | Drama | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 3 | 0 | 2 | 4 | 0 | 0 | 1 | 1 | 0 | 0 |
| 9964 | n12693 | Witter, Frank | 15 | Comedy | 0 | 0 | 0 | 1 | 1 | 2 | 3 | 0 | 1 | 0 | 0 | 1 | 0 | 2 | 1 | 0 | 0 | 2 | 0 | 1 | 0 |
| 10048 | n449 | Jostyn, Jennifer | 15 | Drama | 0 | 0 | 0 | 2 | 0 | 2 | 1 | 0 | 0 | 2 | 0 | 1 | 0 | 3 | 4 | 0 | 0 | 0 | 0 | 0 | 0 |
| 10062 | n9066 | Sucharetza, Marla | 13 | Comedy | 0 | 0 | 0 | 0 | 0 | 1 | 5 | 0 | 0 | 0 | 0 | 2 | 0 | 1 | 2 | 0 | 1 | 1 | 0 | 0 | 0 |
| 10084 | n17356 | Broustal, Sophie | 18 | Drama | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 12 | 3 | 0 | 1 | 0 | 0 | 0 | 1 |
| 10094 | n12151 | Martells, Cynthia | 10 | Romance | 1 | 0 | 0 | 0 | 1 | 3 | 0 | 0 | 0 | 0 | 1 | 2 | 0 | 0 | 2 | 0 | 0 | 0 | 0 | 0 | 0 |
| 10096 | n14533 | Little, Kim | 12 | Comedy | 0 | 0 | 0 | 1 | 0 | 1 | 3 | 0 | 0 | 2 | 0 | 0 | 0 | 3 | 1 | 0 | 0 | 1 | 0 | 0 | 0 |
| 10099 | n15642 | Hada, Michiko | 10 | Drama | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 5 | 3 | 0 | 0 | 0 | 0 | 1 | 0 |
| 10108 | n17569 | Yeryomin, Vladimir | 10 | Drama | 0 | 0 | 0 | 0 | 1 | 2 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 2 | 3 | 0 | 0 | 0 | 0 | 0 | 0 |
| 10206 | n13389 | Russell, Lucy | 11 | Drama | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 1 | 1 | 0 | 1 | 0 | 2 | 3 | 0 | 1 | 0 | 0 | 0 | 0 |
| 10209 | n4981 | Harrington, Cheryl Francis | 12 | Comedy | 0 | 0 | 0 | 0 | 0 | 0 | 5 | 0 | 0 | 1 | 1 | 2 | 0 | 1 | 2 | 0 | 0 | 0 | 0 | 0 | 0 |
| 10259 | n9163 | Koundé, Hubert | 13 | Drama | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 5 | 4 | 0 | 0 | 1 | 0 | 0 | 0 |
| 10361 | n2763 | Schumann, Tanja | 10 | Comedy | 0 | 0 | 1 | 0 | 2 | 0 | 2 | 0 | 2 | 0 | 0 | 0 | 0 | 2 | 0 | 0 | 1 | 0 | 0 | 0 | 0 |
| 10410 | n10589 | Shire, Talia | 12 | Drama | 0 | 0 | 0 | 0 | 0 | 2 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 6 | 0 | 0 | 0 | 1 | 0 | 0 |
| 10415 | n16525 | Winnick, Katheryn | 13 | Horror | 0 | 0 | 0 | 1 | 0 | 2 | 3 | 0 | 0 | 3 | 0 | 0 | 0 | 0 | 2 | 0 | 0 | 1 | 1 | 0 | 0 |
| 10417 | n6906 | Coll, Ivonne | 10 | Thriller | 2 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 4 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 1 |
| 10419 | n16279 | Bonacelli, Paolo | 11 | Comedy | 0 | 0 | 0 | 0 | 0 | 1 | 3 | 0 | 0 | 1 | 0 | 1 | 0 | 3 | 2 | 0 | 0 | 0 | 0 | 0 | 0 |
| 10448 | n14125 | Merrells, Jason | 10 | Drama | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 6 | 0 | 0 | 1 | 1 | 0 | 0 |
| 10451 | n17535 | Choi, Min-su | 13 | Romance | 0 | 0 | 0 | 1 | 0 | 4 | 2 | 0 | 0 | 0 | 0 | 2 | 0 | 2 | 1 | 0 | 1 | 0 | 0 | 0 | 0 |
| 10460 | n1286 | Egan, Peter (I) | 11 | Drama | 0 | 0 | 0 | 0 | 1 | 0 | 2 | 0 | 0 | 0 | 0 | 2 | 0 | 2 | 3 | 0 | 0 | 0 | 0 | 0 | 1 |
| 10487 | n16069 | Taylor, LG | 10 | Drama | 0 | 0 | 0 | 0 | 1 | 0 | 2 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 3 | 0 | 0 | 3 | 0 | 0 | 0 |
| 10609 | n16995 | Makise, Riho | 14 | Drama | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 10 | 3 | 0 | 0 | 0 | 0 | 0 | 0 |
| 10765 | n6957 | Kean, Greg | 11 | Drama | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 1 | 0 | 1 | 0 | 2 | 0 | 1 | 4 | 0 | 0 | 0 | 0 | 0 | 0 |
| 10828 | n13654 | Albertini, Michel | 10 | Thriller | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 3 | 0 | 5 | 1 | 0 | 0 | 0 | 0 | 0 | 0 |
| 10838 | n17434 | Ruslanova, Nina | 10 | Drama | 0 | 0 | 0 | 0 | 1 | 1 | 2 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 3 | 0 | 0 | 0 | 0 | 1 | 0 |
| 10889 | n15693 | Chapman, Sean | 11 | Drama | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 | 0 | 1 | 4 | 0 | 1 | 1 | 0 | 0 | 0 |
| 10891 | n17391 | Schaffel, Lauren | 11 | Family | 0 | 0 | 0 | 0 | 2 | 0 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 3 | 0 | 0 | 0 | 3 | 1 | 0 | 0 |
| 10908 | n4445 | Burke, Carlease (I) | 10 | Drama | 1 | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 3 | 0 | 0 | 1 | 0 | 0 | 0 |
| 10973 | n14486 | Boutsikaris, Dennis | 13 | Drama | 0 | 0 | 0 | 0 | 0 | 2 | 1 | 0 | 0 | 0 | 0 | 2 | 0 | 0 | 7 | 0 | 0 | 0 | 0 | 0 | 1 |
| 11011 | n8466 | Spano, Vincent | 14 | Drama | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 3 | 0 | 1 | 7 | 0 | 0 | 0 | 1 | 0 | 1 |
| 11021 | n15924 | Provenza, Paul | 10 | Comedy | 0 | 0 | 0 | 0 | 0 | 1 | 3 | 0 | 0 | 0 | 0 | 1 | 0 | 3 | 1 | 0 | 0 | 1 | 0 | 0 | 0 |
| 11133 | n11229 | Wilson, Cal (II) | 10 | Comedy | 0 | 0 | 0 | 0 | 0 | 0 | 6 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 0 | 0 | 0 | 0 | 1 | 0 | 1 |
| 11146 | n11086 | Underwood, Jay (I) | 16 | Drama | 0 | 0 | 0 | 1 | 0 | 2 | 1 | 0 | 0 | 0 | 0 | 3 | 0 | 1 | 6 | 0 | 0 | 1 | 0 | 0 | 1 |
| 11161 | n16246 | Sihol, Caroline | 10 | Drama | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 3 | 3 | 0 | 0 | 2 | 0 | 0 | 0 |
| 11162 | n17214 | Gallagher, Frank (II) | 10 | Comedy | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 0 | 0 | 0 | 0 | 1 | 0 | 4 | 0 | 0 | 0 | 3 | 0 | 0 | 0 |
| 11167 | n17440 | Ulyanov, Mikhail | 10 | Drama | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 5 | 4 | 0 | 0 | 0 | 0 | 0 | 0 |
| 11175 | n9429 | Kern, Joey | 10 | Romance | 0 | 0 | 0 | 0 | 0 | 4 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 1 | 0 | 0 | 2 | 0 | 0 | 0 |
| 11176 | n17820 | Menti, Nena | 10 | Drama | 0 | 0 | 0 | 0 | 0 | 2 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 3 | 0 | 0 | 1 | 0 | 1 | 0 |
| 11177 | n10766 | Milos, Sofia | 10 | Comedy | 0 | 0 | 0 | 1 | 1 | 0 | 4 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 2 | 0 | 0 | 0 | 0 | 0 | 0 |
| 11194 | n15231 | Messuri, LoriDawn | 13 | Thriller | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 1 | 0 | 3 | 0 | 3 | 2 | 0 | 0 | 1 | 0 | 0 | 0 |
| 11234 | n17775 | Kianian, Reza | 11 | Thriller | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 0 | 6 | 2 | 0 | 0 | 0 | 0 | 0 | 0 |
| 11255 | n15933 | Idiz, Nurseli | 11 | Drama | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 3 | 5 | 0 | 1 | 0 | 0 | 0 | 0 |
| 11284 | n16916 | Garcia, Aimee | 10 | Drama | 0 | 0 | 1 | 0 | 2 | 1 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 3 | 0 | 0 | 0 | 0 | 0 | 0 |
| 11292 | n15173 | Graham, Julie (I) | 11 | Drama | 0 | 0 | 0 | 0 | 0 | 2 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 7 | 0 | 0 | 0 | 0 | 0 | 0 |
| 11330 | n515 | Bron, Eleanor | 10 | Drama | 1 | 0 | 2 | 0 | 0 | 2 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 2 | 0 | 0 | 0 | 0 | 0 | 1 |
| 11397 | n8804 | Fitzpatrick, Leo | 15 | Drama | 0 | 0 | 1 | 1 | 0 | 1 | 1 | 0 | 1 | 0 | 0 | 2 | 0 | 1 | 3 | 0 | 0 | 1 | 2 | 0 | 1 |
| 11403 | n583 | Kasper, Gary | 12 | Sci-Fi | 0 | 0 | 1 | 1 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 2 | 0 | 2 | 1 | 0 | 1 | 0 | 0 | 0 | 2 |
| 11421 | n14858 | Gorski, Tamara | 10 | Sci-Fi | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 2 | 2 | 0 | 0 | 0 | 0 | 1 | 2 |
| 11436 | n17411 | Fukaura, Kanako | 10 | Drama | 0 | 0 | 1 | 0 | 0 | 3 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 4 | 0 | 0 | 0 | 0 | 1 | 0 |
| 11439 | n17854 | Tork, Hanan | 13 | Romance | 0 | 0 | 0 | 0 | 0 | 3 | 3 | 0 | 0 | 0 | 0 | 0 | 0 | 5 | 2 | 0 | 0 | 0 | 0 | 0 | 0 |
| 11500 | n7103 | Spencer-Nairn, Tara | 12 | Comedy | 1 | 0 | 0 | 0 | 1 | 1 | 4 | 0 | 0 | 1 | 0 | 1 | 0 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
| 11526 | n10084 | Tweeden, Leeann | 11 | Music | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 7 | 0 | 0 | 0 | 0 | 2 | 0 | 0 |
| 11566 | n17613 | Hamilton, Emily | 12 | Drama | 1 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 7 | 0 | 0 | 0 | 0 | 0 | 0 |
| 11627 | n13317 | Ferguson, Tim (I) | 10 | Comedy | 0 | 0 | 0 | 0 | 0 | 0 | 4 | 0 | 0 | 0 | 0 | 0 | 0 | 6 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 11648 | n4041 | Owen, Lloyd | 12 | Romance | 0 | 0 | 0 | 0 | 1 | 2 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 2 | 1 | 0 | 1 | 2 | 0 | 1 | 0 |
| 11678 | n12127 | Drukarova, Dinara | 11 | Drama | 0 | 0 | 0 | 0 | 0 | 3 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 5 | 0 | 0 | 2 | 0 | 0 | 0 |
| 11768 | n1500 | Ryan, Mitch | 12 | Drama | 1 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 3 | 0 | 0 | 3 | 0 | 0 | 1 | 1 | 0 | 1 |
| 11851 | n17847 | Haralambidis, Renos | 11 | Comedy | 0 | 0 | 0 | 0 | 0 | 3 | 5 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 |
| 11881 | n6828 | Porizkova, Paulina | 10 | Comedy | 1 | 0 | 0 | 0 | 0 | 2 | 3 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 |
| 11892 | n16340 | Zappa, Ahmet | 10 | Comedy | 0 | 0 | 0 | 0 | 0 | 0 | 3 | 0 | 0 | 0 | 0 | 1 | 0 | 4 | 0 | 0 | 0 | 1 | 0 | 0 | 1 |
| 11916 | n4902 | Ba, Inday | 10 | Thriller | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 0 | 1 | 0 | 0 | 3 | 0 | 1 | 2 | 0 | 0 | 0 | 0 | 0 | 0 |
| 11935 | n16965 | Okada, Yoshinori | 10 | Drama | 0 | 0 | 0 | 0 | 0 | 1 | 3 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 4 | 0 | 0 | 0 | 0 | 0 | 0 |
| 12071 | n17105 | Ashbourne, Jayne | 14 | Drama | 1 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 4 | 5 | 0 | 1 | 0 | 0 | 0 | 0 |
| 12091 | n17734 | Cass, John (I) | 11 | Comedy | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 4 | 1 | 1 | 0 | 0 | 3 | 0 | 0 |
| 12101 | n10837 | Peldon, Ashley | 13 | Comedy | 2 | 0 | 1 | 0 | 1 | 0 | 3 | 0 | 0 | 0 | 0 | 0 | 0 | 4 | 2 | 0 | 0 | 0 | 0 | 0 | 0 |
| 12126 | n14871 | Speer, Hugo | 11 | Drama | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 3 | 4 | 0 | 0 | 0 | 0 | 1 | 0 |
| 12143 | n11466 | Cray, Ed | 11 | Drama | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 2 | 0 | 3 | 3 | 0 | 0 | 0 | 0 | 0 | 0 |
| 12174 | n4242 | Gibney, Susan | 11 | Drama | 0 | 0 | 0 | 0 | 0 | 2 | 2 | 0 | 0 | 0 | 0 | 3 | 0 | 1 | 3 | 0 | 0 | 0 | 0 | 0 | 0 |
| 12231 | n5947 | Czischek, Elke | 10 | Comedy | 0 | 0 | 0 | 0 | 0 | 0 | 3 | 0 | 3 | 0 | 0 | 1 | 0 | 2 | 1 | 0 | 0 | 0 | 0 | 0 | 0 |
| 12239 | n5866 | Bohm, Marquard | 12 | Thriller | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 4 | 0 | 2 | 2 | 0 | 0 | 1 | 0 | 0 | 1 |
| 12258 | n8410 | Bergeron, Philippe (I) | 13 | Thriller | 0 | 0 | 0 | 0 | 0 | 1 | 2 | 0 | 0 | 1 | 0 | 4 | 0 | 2 | 1 | 0 | 0 | 2 | 0 | 0 | 0 |
| 12271 | n14400 | Rubin, Jennifer | 17 | Thriller | 0 | 1 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 4 | 0 | 2 | 3 | 0 | 1 | 0 | 0 | 0 | 4 |
| 12276 | n5490 | Gillette, Anita | 12 | Comedy | 0 | 0 | 0 | 0 | 0 | 1 | 4 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 4 | 0 | 0 | 1 | 1 | 0 | 0 |
| 12309 | n4044 | Thomson, Kristen | 10 | Drama | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 4 | 0 | 0 | 3 | 0 | 0 | 0 |
| 12368 | n16408 | Gabriela, Marília | 11 | Drama | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 5 | 2 | 0 | 0 | 0 | 1 | 0 | 0 |
| 12371 | n8463 | Davis, William Stanford | 14 | Romance | 0 | 0 | 0 | 0 | 0 | 2 | 1 | 0 | 0 | 1 | 0 | 2 | 0 | 2 | 2 | 0 | 0 | 4 | 0 | 0 | 0 |
| 12402 | n17635 | Vanthielen, Francesca | 12 | Drama | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 4 | 5 | 0 | 0 | 0 | 1 | 0 | 0 |
| 12437 | n14102 | Stewart, Will (I) | 12 | Drama | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 2 | 0 | 3 | 4 | 0 | 0 | 0 | 0 | 0 | 0 |
| 12456 | n9220 | Seigner, Emmanuelle | 14 | Comedy | 2 | 0 | 1 | 0 | 0 | 0 | 3 | 0 | 0 | 0 | 0 | 2 | 0 | 1 | 2 | 0 | 0 | 1 | 0 | 1 | 1 |
| 12642 | n17801 | Hristov, Ivaylo | 10 | Drama | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 2 | 3 | 0 | 0 | 3 | 0 | 0 | 0 |
| 12693 | n8816 | Michaels, Rhino | 11 | Drama | 1 | 0 | 0 | 0 | 0 | 0 | 2 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 3 | 0 | 0 | 0 | 0 | 0 | 2 |
| 12709 | n16370 | Riker, Robin | 11 | Comedy | 1 | 0 | 0 | 0 | 1 | 0 | 3 | 0 | 0 | 0 | 0 | 1 | 0 | 2 | 1 | 0 | 0 | 1 | 1 | 0 | 0 |
| 12716 | n17200 | Georgakis, Nikos | 15 | Drama | 0 | 0 | 0 | 0 | 1 | 2 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 8 | 0 | 0 | 2 | 0 | 0 | 0 |
| 12747 | n17723 | Wang, Zhiwen | 10 | Drama | 0 | 0 | 0 | 0 | 0 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 5 | 0 | 0 | 0 | 0 | 1 | 0 |
| 12772 | n17865 | Mizuki, Arisa | 11 | Drama | 0 | 0 | 0 | 0 | 0 | 1 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 5 | 3 | 0 | 0 | 0 | 0 | 0 | 0 |
| 12827 | n12193 | Rinaldi, Renzo | 11 | Comedy | 0 | 0 | 0 | 0 | 0 | 1 | 3 | 0 | 0 | 1 | 0 | 1 | 0 | 5 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 12934 | n17750 | Bal, Kenan | 11 | Drama | 0 | 0 | 0 | 0 | 0 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 4 | 0 | 2 | 0 | 1 | 1 | 0 |
| 12953 | n12899 | Lavandier, Luc | 11 | Thriller | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 2 | 0 | 7 | 1 | 0 | 0 | 0 | 0 | 0 | 0 |
| 12981 | n16631 | Servais, Manuela | 11 | Drama | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 5 | 3 | 0 | 0 | 2 | 1 | 0 | 0 |
| 12988 | n8419 | Lafferty, Sandra Ellis | 10 | Drama | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 3 | 0 | 1 | 1 | 0 | 0 | 0 |
| 13001 | n16913 | Lim, Kay Tong | 11 | Drama | 0 | 0 | 0 | 0 | 0 | 2 | 3 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 3 | 0 | 0 | 1 | 0 | 1 | 0 |
| 13037 | n14637 | Zahonero, Coraly | 10 | Drama | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 5 | 2 | 0 | 0 | 0 | 1 | 0 | 0 |
| 13232 | n15226 | Cannon, Harold | 16 | Thriller | 0 | 0 | 0 | 2 | 0 | 2 | 0 | 0 | 1 | 2 | 0 | 3 | 0 | 1 | 1 | 0 | 1 | 2 | 0 | 0 | 1 |
| 13244 | n9236 | Smith, Douglas (VI) | 13 | Thriller | 0 | 0 | 0 | 0 | 0 | 1 | 2 | 0 | 0 | 1 | 0 | 3 | 0 | 2 | 3 | 0 | 0 | 0 | 0 | 0 | 1 |
| 13271 | n15578 | Speck, Karsten | 11 | Comedy | 0 | 0 | 0 | 0 | 0 | 1 | 3 | 0 | 0 | 0 | 0 | 1 | 0 | 4 | 2 | 0 | 0 | 0 | 0 | 0 | 0 |
| 13412 | n15769 | Mojica, Monique | 18 | Drama | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 3 | 0 | 0 | 0 | 12 | 0 | 1 |
| 13457 | n16494 | Brochtrup, Bill | 10 | Drama | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 2 | 0 | 1 | 3 | 0 | 0 | 1 | 0 | 0 | 1 |
| 13538 | n7038 | Litz, Nadia | 10 | Drama | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 2 | 0 | 2 | 2 | 0 | 0 | 2 | 0 | 0 | 0 |
| 13560 | n10243 | Massey, Jennifer (I) | 13 | Drama | 0 | 1 | 0 | 0 | 0 | 3 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 4 | 0 | 0 | 3 | 0 | 0 | 0 |
| 13628 | n13260 | May, Roger (I) | 11 | Drama | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 5 | 3 | 0 | 1 | 0 | 1 | 1 | 0 |
| 13650 | n14590 | McMains, Cody | 11 | Drama | 0 | 0 | 1 | 0 | 1 | 0 | 3 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 4 | 0 | 0 | 1 | 0 | 0 | 0 |
| 13692 | n2841 | Cawood, Sarah | 10 | Music | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 7 | 0 | 0 |
| 13699 | n14269 | Lesure, James | 11 | Comedy | 1 | 0 | 0 | 0 | 0 | 1 | 2 | 0 | 1 | 0 | 0 | 1 | 0 | 1 | 1 | 1 | 0 | 1 | 1 | 0 | 0 |
| 13729 | n9749 | Pyper-Ferguson, John | 14 | Drama | 0 | 0 | 0 | 0 | 0 | 2 | 2 | 1 | 0 | 0 | 0 | 2 | 0 | 0 | 4 | 0 | 0 | 1 | 0 | 0 | 2 |
| 13769 | n17834 | Lawlor, Gerri | 10 | Romance | 0 | 0 | 2 | 0 | 1 | 2 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 2 | 0 | 0 | 0 |
| 13837 | n11126 | Baraka, Amiri | 10 | Drama | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 2 | 0 | 0 | 0 | 6 | 0 | 0 |
| 13867 | n8512 | Popowich, Paul | 10 | Drama | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 1 | 0 | 1 | 0 | 1 | 3 | 0 | 0 | 1 | 0 | 0 | 1 |
| 13949 | n11610 | Teale, Owen | 11 | Drama | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 1 | 0 | 1 | 0 | 3 | 2 | 0 | 0 | 0 | 0 | 1 | 1 |
| 13956 | n7713 | Bellar, Clara | 13 | Drama | 0 | 0 | 0 | 0 | 0 | 3 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 4 | 3 | 0 | 0 | 1 | 0 | 0 | 0 |
| 14015 | n16469 | Dollar, Aubrey | 11 | Drama | 0 | 0 | 0 | 0 | 0 | 2 | 1 | 0 | 0 | 0 | 0 | 2 | 0 | 2 | 3 | 0 | 0 | 0 | 1 | 0 | 0 |
| 14050 | n14121 | Puleston-Davies, Ian | 10 | Drama | 0 | 0 | 0 | 0 | 1 | 0 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 4 | 3 | 0 | 0 | 0 | 0 | 0 | 0 |
| 14057 | n10379 | Cryston, Rob | 18 | Adult | 0 | 6 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 11 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 14099 | n9401 | Sparber, Herschel | 10 | Comedy | 1 | 0 | 0 | 1 | 0 | 1 | 2 | 1 | 0 | 0 | 0 | 2 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 |
| 14130 | n13295 | Heo, Jun-ho | 10 | Drama | 0 | 0 | 1 | 1 | 0 | 2 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 2 | 2 | 0 | 0 | 0 | 0 | 0 | 0 |
| 14134 | n15665 | Rosete, Jose | 13 | Drama | 0 | 0 | 0 | 2 | 0 | 1 | 0 | 0 | 2 | 2 | 0 | 2 | 0 | 1 | 2 | 0 | 0 | 1 | 0 | 0 | 0 |
| 14138 | n17809 | Keren, Dror | 11 | Drama | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 7 | 0 | 0 | 1 | 0 | 0 | 0 |
| 14194 | n14006 | Moyer, Stephen (I) | 13 | Drama | 0 | 0 | 0 | 0 | 0 | 2 | 1 | 0 | 0 | 0 | 0 | 2 | 0 | 2 | 5 | 0 | 0 | 0 | 0 | 1 | 0 |
| 14198 | n10733 | Thornton, Sigrid | 12 | Animation | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 1 | 0 | 2 | 0 | 0 | 4 | 1 | 0 | 0 | 2 | 0 | 0 | 0 |
| 14206 | n17883 | Pickhaver, Greig | 11 | Comedy | 0 | 0 | 0 | 0 | 0 | 0 | 9 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 0 | 0 |
| 14217 | n2107 | O'Toole, Matt | 10 | Drama | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 4 | 0 | 1 | 2 | 0 | 0 | 0 |
| 14264 | n13706 | Schuch, Karoline | 11 | Romance | 0 | 0 | 0 | 0 | 0 | 4 | 3 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 3 | 0 | 0 | 0 | 0 | 0 | 0 |
| 14270 | n14246 | Lauren, Val | 11 | Romance | 0 | 0 | 0 | 0 | 0 | 3 | 1 | 0 | 0 | 1 | 0 | 2 | 0 | 1 | 2 | 0 | 0 | 0 | 0 | 1 | 0 |
| 14375 | n11576 | Cozart, Cylk | 14 | Drama | 0 | 0 | 1 | 0 | 1 | 2 | 1 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 5 | 0 | 0 | 0 | 0 | 0 | 2 |
| 14394 | n4239 | Conti, Tom | 12 | Drama | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 4 | 0 | 0 | 0 | 5 | 0 | 0 |
| 14411 | n4395 | Hathaway, Amy (I) | 10 | Romance | 1 | 0 | 0 | 0 | 0 | 2 | 2 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 2 | 0 | 0 | 0 | 0 | 0 | 1 |
| 14421 | n1544 | Ové, Indra | 11 | Sci-Fi | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 3 | 0 | 0 | 2 | 0 | 0 | 4 |
| 14430 | n6860 | Vernon, Kate | 12 | Comedy | 0 | 0 | 0 | 0 | 0 | 1 | 3 | 0 | 0 | 0 | 0 | 3 | 0 | 1 | 2 | 0 | 1 | 1 | 0 | 0 | 0 |
| 14501 | n14582 | Clark, Marsha | 10 | Comedy | 0 | 0 | 0 | 1 | 2 | 0 | 2 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 2 | 0 | 0 | 0 |
| 14575 | n11753 | Conde, Fernando | 10 | Comedy | 0 | 0 | 0 | 0 | 0 | 0 | 6 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 2 | 0 | 0 | 0 | 0 | 0 | 0 |
| 14618 | n12222 | Pickett, Cindy | 10 | Thriller | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 3 | 0 | 2 | 2 | 0 | 1 | 0 | 0 | 0 | 1 |
| 14639 | n5581 | Bell, Kristen (I) | 10 | Drama | 0 | 0 | 1 | 1 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 5 | 0 | 0 | 0 | 0 | 0 | 0 |
| 14713 | n14754 | Pallas, Cécile | 10 | Romance | 0 | 0 | 0 | 0 | 0 | 2 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 4 | 0 | 0 | 1 | 1 | 0 | 0 | 0 |
| 14731 | n13393 | Gidley, Pamela | 14 | Comedy | 1 | 0 | 0 | 0 | 0 | 2 | 3 | 0 | 2 | 1 | 0 | 1 | 0 | 1 | 2 | 0 | 0 | 0 | 1 | 0 | 0 |
| 14738 | n17390 | Praetorius, Friedrich Karl | 10 | Crime | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 4 | 0 | 0 | 2 | 0 | 3 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 14755 | n15059 | Jett, Joan | 12 | Music | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 3 | 0 | 0 | 0 | 1 | 0 | 2 | 2 | 0 | 0 | 1 | 2 | 0 | 0 |
| 14828 | n13582 | Fazira, Erra | 13 | Romance | 0 | 0 | 0 | 0 | 0 | 9 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 14930 | n3567 | Gordon, Pamela (I) | 17 | Drama | 0 | 0 | 0 | 0 | 0 | 1 | 2 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 6 | 0 | 0 | 3 | 1 | 0 | 2 |
| 14936 | n13649 | Solka, Gunnar | 12 | Thriller | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 2 | 0 | 2 | 2 | 0 | 0 | 4 | 0 | 0 | 0 |
| 15022 | n9253 | O'Keefe, Michael | 10 | Comedy | 0 | 0 | 1 | 0 | 0 | 1 | 4 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 | 0 | 0 | 1 | 0 | 0 | 0 |
| 15033 | n473 | Glaser, Paul Michael | 10 | Crime | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 0 | 0 | 0 | 0 | 2 | 1 | 0 | 0 | 3 | 1 | 0 | 0 |
| 15047 | n10153 | Taylor, Regina (I) | 11 | Drama | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 2 | 0 | 0 | 7 | 0 | 0 | 0 | 0 | 0 | 0 |
| 15053 | n15951 | Robinson, Charles (I) | 14 | Drama | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 5 | 1 | 0 | 2 | 0 | 0 | 1 |
| 15071 | n8098 | Guillory, Bennet | 10 | Drama | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 3 | 0 | 0 | 2 | 1 | 0 | 2 |
| 15096 | n12153 | DeLizia, Cara | 10 | Comedy | 0 | 0 | 0 | 1 | 1 | 0 | 3 | 0 | 0 | 0 | 0 | 1 | 0 | 2 | 2 | 0 | 0 | 0 | 0 | 0 | 0 |
| 15098 | n15557 | Moody, Ron | 10 | Music | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 2 | 0 | 0 | 0 | 0 | 0 | 3 | 1 | 0 | 0 | 2 | 0 | 0 | 0 |
| 15177 | n17618 | Park, Shin-yang | 10 | Romance | 0 | 0 | 0 | 1 | 0 | 2 | 1 | 0 | 1 | 1 | 0 | 0 | 0 | 3 | 1 | 0 | 0 | 0 | 0 | 0 | 0 |
| 15197 | n7505 | Vogt, Paul (II) | 10 | Comedy | 0 | 0 | 0 | 0 | 3 | 1 | 3 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 1 | 0 | 0 |
| 15227 | n11149 | Cowell, Brendan | 10 | Drama | 2 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 4 | 0 | 0 | 1 | 0 | 0 | 1 |
| 15263 | n15931 | Alasya, Zeki | 10 | Comedy | 0 | 0 | 0 | 0 | 0 | 2 | 6 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 0 | 0 | 0 | 0 | 0 | 0 |
| 15270 | n16567 | Franco, Jesus | 11 | Horror | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 4 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 5 | 0 | 0 |
| 15363 | n7364 | Dapkunaite, Ingeborga | 12 | Drama | 1 | 0 | 0 | 0 | 0 | 0 | 2 | 0 | 1 | 0 | 0 | 1 | 0 | 1 | 6 | 0 | 0 | 0 | 0 | 0 | 0 |
| 15381 | n9672 | Klein, Gérard (I) | 10 | Comedy | 0 | 0 | 0 | 0 | 1 | 0 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 5 | 0 | 0 | 0 | 0 | 1 | 1 | 0 |
| 15506 | n4974 | Montgomery, Judith | 10 | Thriller | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 0 | 0 | 0 | 0 | 2 | 0 | 3 | 1 | 0 | 0 | 1 | 0 | 0 | 1 |
| 15513 | n10157 | Ray, Connie | 10 | Drama | 0 | 0 | 1 | 0 | 0 | 1 | 3 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 3 | 0 | 0 | 0 | 0 | 0 | 1 |
| 15541 | n17196 | Bocher, Christian | 10 | Drama | 2 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 2 | 0 | 0 | 3 | 0 | 0 | 0 |
| 15560 | n5713 | Kelly, Lisa Robin | 10 | Comedy | 0 | 0 | 0 | 0 | 0 | 0 | 5 | 0 | 1 | 1 | 0 | 1 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 |
| 15582 | n17797 | Bettinger, Manfred | 11 | Comedy | 0 | 0 | 0 | 0 | 0 | 0 | 4 | 0 | 0 | 0 | 0 | 0 | 0 | 7 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 15658 | n248 | Teyssier, Agathe | 14 | Drama | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 6 | 3 | 0 | 0 | 3 | 0 | 0 | 0 |
| 15724 | n6999 | Walter, Lisa Ann | 11 | Comedy | 0 | 0 | 0 | 1 | 0 | 0 | 6 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 | 0 | 0 | 1 | 0 | 0 | 0 |
| 15732 | n17904 | Kuusniemi, Matti | 10 | Horror | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 0 | 0 | 3 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 2 | 0 | 0 | 1 |
| 15736 | n7453 | Malik, Art | 18 | Drama | 1 | 0 | 0 | 0 | 0 | 1 | 2 | 0 | 0 | 0 | 0 | 3 | 0 | 1 | 7 | 0 | 0 | 0 | 3 | 0 | 0 |
| 15781 | n8763 | Steen, Jessica | 12 | Thriller | 0 | 0 | 0 | 1 | 0 | 0 | 2 | 0 | 0 | 0 | 0 | 3 | 0 | 2 | 0 | 0 | 0 | 3 | 1 | 0 | 0 |
| 15804 | n16495 | Mangum, Meagan | 11 | Drama | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 2 | 0 | 0 | 5 | 0 | 0 | 1 |
| 15868 | n13638 | Knaack, Pamela | 13 | Drama | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 0 | 2 | 0 | 0 | 2 | 0 | 3 | 4 | 0 | 0 | 0 | 0 | 0 | 0 |
| 15893 | n14022 | Benesch, Gabriela | 10 | Comedy | 0 | 0 | 1 | 1 | 0 | 0 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 3 | 2 | 0 | 0 | 0 | 0 | 0 | 1 |
| 15932 | n11288 | Bohne, Bruce | 12 | Drama | 0 | 0 | 0 | 0 | 0 | 1 | 3 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 5 | 0 | 0 | 1 | 0 | 0 | 0 |
| 15950 | n16718 | Gray, Erin | 16 | Drama | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 2 | 0 | 6 | 4 | 0 | 0 | 0 | 2 | 0 | 0 |
| 16025 | n13334 | Slater, Kelly | 10 | Action | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 2 | 0 | 0 | 0 | 0 | 5 | 0 | 0 |
| 16031 | n17884 | Moskona, Aryeh | 10 | Drama | 0 | 0 | 0 | 0 | 0 | 2 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 5 | 0 | 0 | 1 | 0 | 0 | 0 |
| 16050 | n17737 | Zhou, Xun | 10 | Drama | 1 | 0 | 0 | 0 | 0 | 3 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 4 | 0 | 0 | 0 | 0 | 0 | 0 |
| 16062 | n15773 | Timbrook, Corby | 11 | Thriller | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 1 | 1 | 0 | 4 | 0 | 1 | 2 | 0 | 0 | 0 | 0 | 0 | 0 |
| 16089 | n8081 | Winters, Time | 18 | Thriller | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 1 | 0 | 2 | 0 | 3 | 0 | 2 | 2 | 0 | 1 | 3 | 0 | 0 | 2 |
| 16120 | n496 | McIntyre, Marilyn | 11 | Drama | 0 | 0 | 0 | 0 | 0 | 3 | 0 | 0 | 0 | 0 | 0 | 3 | 0 | 1 | 4 | 0 | 0 | 0 | 0 | 0 | 0 |
| 16140 | n11312 | Robinson, Bumper | 15 | Comedy | 0 | 0 | 0 | 0 | 0 | 0 | 4 | 0 | 0 | 0 | 2 | 1 | 0 | 2 | 4 | 0 | 0 | 0 | 0 | 0 | 2 |
| 16160 | n15571 | Von Teese, Dita | 11 | Romance | 0 | 1 | 0 | 0 | 0 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 4 | 1 | 0 | 0 | 3 | 0 | 0 | 0 |
| 16170 | n10648 | Fields, Edith (I) | 10 | Drama | 0 | 0 | 0 | 0 | 0 | 0 | 3 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 4 | 0 | 0 | 1 | 0 | 0 | 0 |
| 16221 | n13628 | Sellien, Rainer | 13 | Drama | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 3 | 0 | 0 | 2 | 0 | 2 | 5 | 0 | 0 | 0 | 0 | 0 | 0 |
| 16265 | n17907 | Harry, 'Horny' | 17 | Adult | 0 | 16 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 |
| 16304 | n13859 | Bamman, Gerry | 10 | Drama | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 4 | 0 | 0 | 2 | 0 | 0 | 0 |
| 16325 | n7449 | Akinnuoye-Agbaje, Adewale | 12 | Drama | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 2 | 1 | 2 | 2 | 0 | 0 | 0 | 1 | 1 | 0 |
| 16394 | n13128 | Valentín, Juan | 12 | Action | 0 | 0 | 0 | 8 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 1 | 0 | 0 | 0 | 0 | 0 | 0 |
| 16418 | n16116 | Saeki, Hinako | 13 | Horror | 0 | 0 | 0 | 0 | 0 | 2 | 0 | 0 | 0 | 4 | 0 | 2 | 0 | 1 | 3 | 0 | 0 | 0 | 1 | 0 | 0 |
| 16424 | n14088 | Shepherd, Suzanne | 10 | Drama | 0 | 0 | 0 | 0 | 0 | 1 | 2 | 0 | 2 | 0 | 0 | 0 | 0 | 0 | 3 | 0 | 0 | 2 | 0 | 0 | 0 |
| 16535 | n10822 | Treviño, Marco Antonio | 10 | Romance | 1 | 0 | 0 | 1 | 1 | 2 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 3 | 0 | 0 | 0 | 1 | 0 | 0 | 0 |
| 16567 | n15812 | Carol, Jean (I) | 12 | Drama | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 4 | 4 | 0 | 0 | 1 | 0 | 0 | 0 |
| 16731 | n1250 | Wong, Benedict | 12 | Drama | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 3 | 0 | 2 | 4 | 0 | 0 | 0 | 0 | 0 | 0 |
| 16823 | n16583 | Coppola, Alicia | 10 | Drama | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 2 | 5 | 0 | 0 | 0 | 0 | 0 | 2 |
| 16870 | n12865 | Delfino, Majandra | 11 | Comedy | 1 | 0 | 0 | 0 | 1 | 1 | 3 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 2 | 0 | 0 | 0 | 0 | 0 | 0 |
| 17008 | n14459 | Vincent, Jan-Michael | 12 | Drama | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 4 | 2 | 0 | 0 | 1 | 0 | 0 | 1 |
| 17045 | n3138 | Phillips, Wendy (I) | 10 | Drama | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 4 | 0 | 0 | 2 | 0 | 0 | 0 |
| 17063 | n5739 | Walker, Ally | 11 | Comedy | 1 | 0 | 1 | 0 | 0 | 1 | 3 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 2 | 0 | 0 | 0 | 1 | 0 | 0 |
| 17081 | n15495 | Ng, Carrie | 12 | Drama | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 5 | 4 | 0 | 0 | 0 | 0 | 0 | 0 |
| 17142 | n17298 | Reema | 16 | Romance | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 13 | 1 | 0 | 0 | 0 | 0 | 0 | 0 |
| 17145 | n14111 | LeBlanc, Sadie | 10 | Romance | 0 | 0 | 0 | 0 | 1 | 2 | 0 | 1 | 1 | 0 | 0 | 1 | 0 | 1 | 2 | 0 | 0 | 1 | 0 | 0 | 0 |
| 17181 | n8868 | Avedikian, Serge | 15 | Drama | 1 | 0 | 0 | 0 | 0 | 1 | 2 | 0 | 0 | 0 | 0 | 1 | 0 | 7 | 3 | 0 | 0 | 0 | 0 | 0 | 0 |
| 17202 | n8037 | Prodan, Andrea | 11 | Drama | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 4 | 6 | 0 | 0 | 0 | 0 | 0 | 0 |
| 17245 | n7311 | Lemke, Anthony | 12 | Romance | 0 | 0 | 1 | 0 | 0 | 2 | 1 | 0 | 0 | 1 | 0 | 2 | 0 | 1 | 1 | 0 | 0 | 2 | 0 | 0 | 1 |
| 17262 | n16226 | Madison, Elina | 10 | Horror | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 4 | 0 | 2 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 |
| 17282 | n16272 | Cao, Jorge | 10 | Drama | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 5 | 3 | 0 | 0 | 0 | 0 | 0 | 0 |
| 17304 | n7093 | Landes, Michael | 10 | Fantasy | 0 | 0 | 2 | 1 | 0 | 0 | 2 | 0 | 0 | 1 | 0 | 0 | 0 | 2 | 1 | 0 | 0 | 0 | 0 | 1 | 0 |
| 17308 | n17740 | Yoshiyuki, Yumi | 12 | Horror | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 5 | 0 | 0 | 0 | 2 | 1 | 0 | 0 | 1 | 0 | 0 | 1 |
| 17333 | n14457 | Janniche, Lisbeth | 10 | NaN | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 8 | 0 | 0 | 0 | 0 | 2 | 0 | 0 |
| 17366 | n10268 | Roth, Andrea | 17 | Thriller | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 1 | 0 | 0 | 6 | 0 | 1 | 5 | 0 | 0 | 0 | 0 | 0 | 2 |
| 17432 | n13307 | Doyle, John (I) | 11 | Comedy | 0 | 0 | 0 | 0 | 0 | 0 | 9 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 0 | 0 |
| 17440 | n16798 | Quartaroli, Peter | 10 | Romance | 0 | 0 | 0 | 0 | 0 | 3 | 1 | 0 | 1 | 0 | 0 | 1 | 0 | 1 | 1 | 0 | 1 | 1 | 0 | 0 | 0 |
| 17467 | n13581 | Hassan, Jalaluddin | 14 | Romance | 0 | 0 | 0 | 0 | 0 | 6 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 5 | 0 | 0 | 0 | 0 | 0 | 1 |
| 17476 | n8501 | Rogers, AnnieScott | 13 | Romance | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 5 | 1 | 0 | 0 | 4 | 0 | 0 | 0 |
| 17567 | n3616 | Downing, Sara (I) | 11 | Horror | 0 | 0 | 1 | 0 | 0 | 2 | 1 | 0 | 0 | 3 | 0 | 3 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
The diameter of a graph is defined as the maximum distance between any pair of nodes in the graph. It is the longest shortest path between any two nodes in the graph.
It provides insight into the overall connectivity of a graph. A small diameter indicates that the graph is well-connected, with short paths between most pairs of nodes. A large diameter, on the other hand, suggests that the graph may be more fragmented or sparsely connected, with longer distances between many pairs of nodes.
The diameter can also be used to estimate the efficiency of certain algorithms, such as those that rely on shortest path calculations. In general, smaller diameters are preferred for these types of algorithms as they can improve their overall performance.
# Calculate the unweighted diameter of each subgraph
for i, sg in enumerate(subgraphs):
diameter = sg.diameter(directed=False)
print(f"Subgraph {i+1} diameter: {diameter}")
Subgraph 1 diameter: 16 Subgraph 2 diameter: 8 Subgraph 3 diameter: 6 Subgraph 4 diameter: 1 Subgraph 5 diameter: 8 Subgraph 6 diameter: 1 Subgraph 7 diameter: 2 Subgraph 8 diameter: 4 Subgraph 9 diameter: 1 Subgraph 10 diameter: 1 Subgraph 11 diameter: 1 Subgraph 12 diameter: 1 Subgraph 13 diameter: 1 Subgraph 14 diameter: 1 Subgraph 15 diameter: 2 Subgraph 16 diameter: 1 Subgraph 17 diameter: 1 Subgraph 18 diameter: 1 Subgraph 19 diameter: 1
From the results we can see that the diameter of each subgraph is relatively small, with most subgraphs having a diameter of 1. This suggests that the actors in each subgraph are relatively well-connected, with no major gaps in the network. Subgraphs 1, 5, and 8 have larger diameters, indicating that some actors in those subgraphs may be more distant from others, but overall the diameters are quite small.
It's important to keep in mind that this analysis only looks at the unweighted graph, so it doesn't take into account the strengths of connections between actors. Especially, the subgraphs with many nodes have higher diameters, since subgraph 1 has the highest number of nodes of 17455 and number of edges: 286911 it looks like that these are realtively well connected actors.
# Calculate the inverted weighted diameter of each subgraph
for i, sg in enumerate(subgraphs_inv):
diameter = sg.diameter(directed=False, weights='weight')
print(f"Subgraph {i+1} weighted diameter: {diameter}")
Subgraph 1 weighted diameter: 6.373464912280701 Subgraph 2 weighted diameter: 3.75 Subgraph 3 weighted diameter: 3.0 Subgraph 4 weighted diameter: 0.11111111111111116 Subgraph 5 weighted diameter: 3.833333333333333 Subgraph 6 weighted diameter: 0.5 Subgraph 7 weighted diameter: 0.6666666666666665 Subgraph 8 weighted diameter: 1.025 Subgraph 9 weighted diameter: 0.09090909090909083 Subgraph 10 weighted diameter: 0.16666666666666674 Subgraph 11 weighted diameter: 0.5 Subgraph 12 weighted diameter: 0.19999999999999996 Subgraph 13 weighted diameter: 0.25 Subgraph 14 weighted diameter: 0.10000000000000009 Subgraph 15 weighted diameter: 0.6111111111111112 Subgraph 16 weighted diameter: 0.5 Subgraph 17 weighted diameter: 0.5 Subgraph 18 weighted diameter: 0.5 Subgraph 19 weighted diameter: 0.10000000000000009
These results give us an idea of the "spread" of each subgraph, taking into account the weights of the edges. For example, subgraphs 1 with a larger diameter may be more spread out and have longer paths between nodes, while subgraphs with a smaller diameter 4,10,14,19 may be more tightly connected.This measure is useful to understand how well-connected a network is and how efficiently information or resources can flow through it. A smaller diameter indicates a more connected network, whereas a larger diameter suggests that it may take longer for information to propagate across the network.
Now, let's look at the weighted graph for Subgraph 1. We see that the weighted diameter of Subgraph 1 is 6.373464912280701. This means that the longest shortest path between any two nodes in Subgraph 1, taking into account the weights of the edges, has a length of 6.373464912280701.This indicates that there are some nodes within that subgraph that are relatively far apart from each other in terms of their weighted distances
The difference between the unweighted and weighted diameters highlights the importance of considering edge weights when analyzing graphs. In the unweighted graph, we only know which nodes are connected, but we don't have any information about the strength or importance of those connections. However, in the weighted graph, we can see that some edges are stronger than others, which can have a significant impact on the overall structure and behavior of the graph.
for i, sg in enumerate(subgraphs):
avg_path_length = sg.average_path_length(directed=False)
print(f"Subgraph {i+1} avg shortest path length without weight: {avg_path_length:.2f}")
Subgraph 1 avg shortest path length without weight: 4.89 Subgraph 2 avg shortest path length without weight: 3.33 Subgraph 3 avg shortest path length without weight: 3.23 Subgraph 4 avg shortest path length without weight: 1.00 Subgraph 5 avg shortest path length without weight: 3.30 Subgraph 6 avg shortest path length without weight: 1.00 Subgraph 7 avg shortest path length without weight: 1.33 Subgraph 8 avg shortest path length without weight: 1.91 Subgraph 9 avg shortest path length without weight: 1.00 Subgraph 10 avg shortest path length without weight: 1.00 Subgraph 11 avg shortest path length without weight: 1.00 Subgraph 12 avg shortest path length without weight: 1.00 Subgraph 13 avg shortest path length without weight: 1.00 Subgraph 14 avg shortest path length without weight: 1.00 Subgraph 15 avg shortest path length without weight: 1.33 Subgraph 16 avg shortest path length without weight: 1.00 Subgraph 17 avg shortest path length without weight: 1.00 Subgraph 18 avg shortest path length without weight: 1.00 Subgraph 19 avg shortest path length without weight: 1.00
The shortest path length = is the minimum number of edges that need to be traversed to get from one node to another node in the graph.
The average shortest path length with weight for each subgraph can be used to draw conclusions about the connectivity and structure of the subgraphs:
The results suggest that the average shortest path length varies widely among the connected components, ranging from 1.0 to 4.89, which indicates that the network may be relatively disconnected in some areas and more connected in others.
Overall, analyzing the average shortest path length with weight for each subgraph can help us understand the structure and connectivity of the graph at a more granular level.
# Using the inverse weights to analyse the average path length
for i, sg in enumerate(subgraphs_inv):
avg_path_length = sg.average_path_length(directed=False,weights='weight')
print(f"Subgraph {i+1} avg shortest path length with weight: {avg_path_length:.2f}")
Subgraph 1 avg shortest path length with weight: 1.77 Subgraph 2 avg shortest path length with weight: 1.58 Subgraph 3 avg shortest path length with weight: 1.53 Subgraph 4 avg shortest path length with weight: 0.08 Subgraph 5 avg shortest path length with weight: 1.57 Subgraph 6 avg shortest path length with weight: 0.50 Subgraph 7 avg shortest path length with weight: 0.44 Subgraph 8 avg shortest path length with weight: 0.54 Subgraph 9 avg shortest path length with weight: 0.09 Subgraph 10 avg shortest path length with weight: 0.15 Subgraph 11 avg shortest path length with weight: 0.50 Subgraph 12 avg shortest path length with weight: 0.20 Subgraph 13 avg shortest path length with weight: 0.25 Subgraph 14 avg shortest path length with weight: 0.10 Subgraph 15 avg shortest path length with weight: 0.41 Subgraph 16 avg shortest path length with weight: 0.50 Subgraph 17 avg shortest path length with weight: 0.50 Subgraph 18 avg shortest path length with weight: 0.50 Subgraph 19 avg shortest path length with weight: 0.10
The weighted average shortest path length takes into account the weight of the edges in the graph, while the unweighted average shortest path length does not.
We can see that for most subgraphs, the weighted average shortest path length is much smaller than the unweighted average shortest path length. This is because in many cases, the shortest path between two nodes in a subgraph may not be the one with the fewest number of edges, but rather the one with the lowest weight.
For example, in Subgraph 4, the unweighted average shortest path length is 1.00, which means that the average number of edges between any two nodes in the subgraph is one. However, the weighted average shortest path length is only 0.08, which means that on average, the actual distance between any two nodes in the subgraph is much smaller, taking into account the weights of the edges.
The unweighted average shortest path length in subgraph 1 is 4.89, which means that on average, it takes 4.89 hops to get from one node to another in the subgraph if we don't consider the weight of the edges.However, the weighted average shortest path length in subgraph 1 is 1.77, which means that on average, it takes 1.77 units of weight to get from one node to another in the subgraph. This could suggest that subgraph 1 has a high degree of connectivity, with many nodes closely connected to one another, but with relatively small weights on the edges between them. It could also suggest that the subgraph has a well-defined center or hub, with many nodes connected to that central node, and relatively few long-distance connections between nodes on the periphery of the subgraph.
# Get the value counts of movies_95_04 column
counts = nodes['movies_95_04'].value_counts()
# Create a new DataFrame with counts as a column
counts_df = pd.DataFrame({'Number of Actors': counts})
# Sort the DataFrame by the index (which is the number of movies)
counts_df = counts_df.sort_index()
pd.set_option('display.max_columns', None)
# Print the counts in a tabular format
print(counts_df)
# Create a line graph of the value counts using Plotly
fig = go.Figure(data=go.Scatter(x=counts_df.index, y=counts_df['Number of Actors'], mode='lines', line=dict(color='purple')))
#fig.update_layout(title='Distribution of Number of Movies Made by Actors (1995-2004)', xaxis_title='Number of Movies', yaxis_title='Number of Actors')
#pio.show(fig)
Number of Actors 10 2302 11 1853 12 1570 13 1322 14 1149 .. ... 429 1 435 1 460 1 467 1 540 1 [194 rows x 1 columns]
It is evident from both the graph and the table that the majority of actors were cast in 10 movies, with a sharp decline in the number of movies as the number of castings increases beyond that.
# Counting the number of genres the actors participated in
nodes['no_of_genres'] = nodes['genres'].str.count(',') + 1
The result below shows the number of actors in the dataset grouped by the number of genres they appeared in. For example, there are 3107 actors who appeared in 6 genres, 2691 actors who appeared in 7 genres, and so on. The result is sorted in ascending order based on the count of actors in the respective genre, so it shows that there are more actors who appeared in a smaller number of genres, with a gradual decrease as the number of genres increases.
genre_counts = nodes[['no_of_genres']].value_counts().sort_values(ascending=False)
print(genre_counts)
no_of_genres 6 3107 7 2691 5 2564 8 2056 4 1657 9 1401 3 970 2 916 10 831 11 518 12 342 13 222 14 114 1 94 15 48 16 33 17 9 18 3 19 1 dtype: int64
import plotly.express as px
# Compute the genre counts
genre_counts = nodes['no_of_genres'].value_counts().sort_index()
# Create a dataframe for the counts
df = pd.DataFrame({'no_of_genres': genre_counts.index, 'count': genre_counts.values})
# Create the line plot
fig = px.line(df, x='no_of_genres', y='count', title='Genre Counts')
#fig.update_layout(xaxis_title='Number of Genres', yaxis_title='Count')
# Show the plot
#fig.show()
Based on the output of the code, we can see that the majority of actors in the dataset have between 5 and 8 genres, with the most common number of genres being 6 (with 3107 actors having 6 genres). This suggests that most actors have a relatively diverse set of genres they have worked in.
On the other end of the spectrum, there are only a few actors with 17, 18, or 19 genres, suggesting that it is relatively rare for an actor to have worked in such a diverse range of genres.
# convert 'movies_95_04' column to integer data type
df_with_genres['movies_95_04'] = df_with_genres['movies_95_04'].astype(int)
# get distribution of number of movies by actor
movies_by_actor = df_with_genres.groupby('ActorName')['movies_95_04'].sum()
# get distribution of number of genres by actor
# convert 'main_genre' column to string data type
df_with_genres['main_genre'] = df_with_genres['main_genre'].astype(str)
genres_by_actor = df_with_genres.groupby('ActorName')['main_genre'].nunique()
import plotly.express as px
genre_counts = df_with_genres['main_genre'].value_counts().sort_values(ascending=False)
sorted_genres = genre_counts.index.tolist()
sorted_df = df_with_genres.sort_values(by=['main_genre'], key=lambda x: x.map(sorted_genres.index))
fig = px.histogram(sorted_df, x='main_genre', nbins=len(df_with_genres['main_genre'].unique()), color='main_genre',
color_discrete_sequence=px.colors.qualitative.Pastel1)
#fig.update_layout(title='Distribution of Actors in Main Genre',xaxis_title='Main Genre',yaxis_title='Number of Actors')
#fig.show()
Use the columns of the genres to Create a distribution of the different genres.
cols_to_convert = df_with_genres.columns.drop(['id', 'ActorName','main_genre'])
df_with_genres[cols_to_convert] = df_with_genres[cols_to_convert].apply(pd.to_numeric, errors='coerce')
# create subplots
fig = sp.make_subplots(rows=9, cols=4, subplot_titles=df_with_genres.columns[3:])
# loop over columns and add histogram
for i, col in enumerate(df_with_genres.columns[3:]):
row_num = (i // 4) + 1
col_num = (i % 4) + 1
fig.add_trace(go.Histogram(x=df_with_genres[col], name=col), row=row_num, col=col_num)
# update layout
#fig.update_layout(height=2000, width=1000, title='Distribution of Genres in Movies')
# show plot
#fig.show()
import plotly.graph_objects as go
# create a new dataframe with only the columns of interest
df_genres_count = df_with_genres.iloc[:, 4:25].sum()
# sort genres by count
df_genres_count = df_genres_count.sort_values(ascending=False)
# set custom color palette
colors = ['#00adb5', '#393e46', '#222831', '#eeeeee', '#ffd369', '#f8b500', '#f6416c', '#ff7b25', '#1a1a2e', '#3282b8','#CE99DE','#9f15f3','#390825','#352331','#bc366e','#314b7e','#823814','#4a4788','#2b303b','#3a9b9e']
# create a bar chart for the counts of each genre
fig = go.Figure(go.Bar(x=df_genres_count.index, y=df_genres_count.values, marker_color=colors))
#fig.update_layout(title='Number of Movies per Genre Count from all actors', xaxis_title='Genre', yaxis_title='Count')
#fig.show()
NULL: It's possible that this category represents movies that do not have a specific genre or that the genre information is missing or incomplete. It's also possible that this category includes movies from various genres that are not categorized correctly.
Drama:This is a broad category that can include many different types of movies that focus on serious or emotional themes. Drama movies often have strong character development and can appeal to a wide audience. It's possible that the high number of drama movies reflects the popularity of this genre among filmmakers and audiences.
Adult: This category likely includes movies that are intended for mature audiences and may contain explicit content. It's possible that these movies are very short movies and have more movies available.
Comedy: This is a popular genre. Comedy movies can appeal to a wide audience and may be more likely to generate high box office revenues than other genres. It's possible that the high number of comedy movies reflects the popularity of this genre among filmmakers and audiences.
It's important to note that the number of movies in a particular genre does not necessarily reflect the quality or popularity of those movies.
Obtain three vectors with the degree, betweeness and closeness for each vertex of the actors' graph.
df_with_genres.head(3)
| id | ActorName | movies_95_04 | main_genre | Mystery | Adult | Fantasy | Action | Family | Romance | Comedy | Music | Crime | Horror | Animation | Thriller | Musical | NULL | Drama | Western | Adventure | Short | Documentary | War | Sci-Fi | betweenness_weigth | pagerank_weigth | eigenvector_weight | closeness_weight | neighbors | degree | betweenness | closeness | subgraph_num | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | n15629 | Rudder, Michael (I) | 12 | Thriller | 0 | 0 | 1 | 1 | 0 | 1 | 1 | 0 | 0 | 1 | 0 | 2 | 0 | 2 | 1 | 0 | 0 | 0 | 0 | 1 | 1 | 551.325397 | 0.000057 | 0.000283 | 0.658297 | [431, 672, 717, 823, 858, 911, 1787, 3674, 391... | 36 | 3929.012117 | 0.110066 | 0 |
| 1 | n5026 | Morgan, Debbi | 16 | Drama | 0 | 0 | 0 | 0 | 0 | 2 | 2 | 0 | 0 | 2 | 0 | 0 | 0 | 3 | 6 | 0 | 0 | 0 | 1 | 0 | 0 | 949.411508 | 0.000056 | 0.000261 | 0.667708 | [352, 452, 1638, 2110, 2170, 3419, 4879, 5250,... | 23 | 21716.283252 | 0.122167 | 0 |
| 2 | n11252 | Bellows, Gil | 33 | Drama | 2 | 0 | 1 | 0 | 1 | 6 | 6 | 0 | 0 | 1 | 0 | 4 | 0 | 2 | 7 | 0 | 0 | 2 | 1 | 0 | 0 | 19206.960317 | 0.000058 | 0.000055 | 0.689974 | [3188, 4430, 4452, 4671, 5324, 5379, 5902, 652... | 22 | 13283.000044 | 0.112943 | 0 |
Obtain the list of the 20 actors with the largest degree centrality. It can be useful to show a list with the degree, the name of the actor, the number of movies, the main genre, and the number of genres in which the actor has participated.
df_with_genres.sort_values(by='degree', ascending=False).head(1)
| id | ActorName | movies_95_04 | main_genre | Mystery | Adult | Fantasy | Action | Family | Romance | Comedy | Music | Crime | Horror | Animation | Thriller | Musical | NULL | Drama | Western | Adventure | Short | Documentary | War | Sci-Fi | betweenness_weigth | pagerank_weigth | eigenvector_weight | closeness_weight | neighbors | degree | betweenness | closeness | subgraph_num | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 12147 | n162 | Davis, Mark (V) | 540 | Adult | 0 | 429 | 1 | 1 | 0 | 0 | 3 | 0 | 1 | 0 | 0 | 0 | 0 | 92 | 6 | 0 | 0 | 1 | 5 | 0 | 1 | 1.703766e+07 | 0.000427 | 0.980388 | 0.835476 | [83, 86, 104, 131, 137, 139, 142, 181, 184, 29... | 784 | 184760.341185 | 0.104901 | 0 |
Davis Mark as seen in the below table, is an American actor who is known for his work in adult films. He has been active in the adult film industry since the 1980s and has appeared in a large number of adult films. He has played in 10 different genres, including Adult, which is the main genre he is associated with.
The degree of the David Mark node represents the number of other actors they have worked with. In the co-starring graph of the movies dataset, Davis Mark has the highest degree among all actors in the Adult genre. This means that he has worked with a large number of other actors in this genre, which may suggest that he is a prolific and well-connected actor within the adult film industry.
It's important to note that the degree centrality measure only takes into account the number of connections a node has, without considering the importance or influence of the other nodes it is connected to. Therefore, while Davis Mark has the highest degree in the Adult genre, there may be other actors who have more important or influential connections within the industry.
# Get the neighbors of n162
neighbors = g.neighbors('n162')
# Create a subgraph with n2108 and its neighbors
subgraph_Mark = g.subgraph(['n162'] + neighbors)
# Set the ActorName as the vertex label for node 'n162'
#subgraph_Mark.vs.find('n162')['label'] = nodes.loc[nodes['id'] == 'n162', 'ActorName'].values[0]
# Plot the subgraph without vertex labels
visual_style = {}
visual_style["vertex_size"] = 20
visual_style["bbox"] = (800, 800)
visual_style["margin"] = 100
#visual_style["vertex_label"] = ''
#ig.plot(subgraph_Mark, **visual_style)
# Get the degree centrality of each actor
degree = g.degree()
# Create a dataframe with the actor ids and their degree centrality
#degree_df = pd.DataFrame({'id': g.vs['name'], 'Degree': degree})
# Merge the degree dataframe with the nodes dataframe to get additional information
merged_df = pd.merge(pd.DataFrame({'id': g.vs['name'], 'Degree': g.degree()}), nodes, on='id')
# Split the genres column into number of genres and add as a new column
merged_df['no_of_genres'] = merged_df['genres'].str.count(',') + 1
# Sort the merged dataframe by degree centrality in descending order and get the top 20 actors
top_actors = merged_df.sort_values('Degree', ascending=False).head(20).reset_index(drop=True)
# Add a ranking column
top_actors['Rank'] = top_actors.index + 1
# Reorder the columns
top_actors = top_actors[['Rank', 'Degree', 'ActorName', 'movies_95_04', 'main_genre', 'no_of_genres']]
# Print the top actors with their degree, name, number of movies, main genre, and number of genres participated in
print(top_actors)
Rank Degree ActorName movies_95_04 main_genre no_of_genres 0 1 784 Davis, Mark (V) 540 Adult 10 1 2 610 Sanders, Alex (I) 467 Adult 10 2 3 599 North, Peter (I) 460 Adult 8 3 4 584 Marcus, Mr. 435 Adult 6 4 5 561 Tedeschi, Tony 364 Adult 11 5 6 555 Dough, Jon 300 Adult 8 6 7 545 Stone, Lee (II) 403 Adult 7 7 8 533 Voyeur, Vince 370 Adult 10 8 9 500 Lawrence, Joel (II) 315 Adult 7 9 10 493 Steele, Lexington 429 Adult 8 10 11 490 Ashley, Jay 309 Adult 8 11 12 475 Boy, T.T. 336 Adult 8 12 13 471 Jeremy, Ron 280 Adult 14 13 14 471 Cannon, Chris (III) 287 Adult 9 14 15 463 Bune, Tyce 267 Adult 5 15 16 457 Hanks, Tom 75 Family 13 16 17 451 Michaels, Sean 252 Adult 6 17 18 450 Stone, Kyle 278 Adult 8 18 19 438 Hardman, Dave 319 Adult 7 19 20 428 Surewood, Brian 244 Adult 6
High degree centrality in actors means that they have appeared in a large number of movies or have been involved in a large number of collaborations with other actors. This can indicate their popularity, influence, and potentially their expertise or skill in acting. It may also suggest that they have a wide range of connections within the movie industry, which can be beneficial for their career advancement. However, it's important to note that high degree centrality alone does not necessarily guarantee success or talent, and other factors such as acting ability, personal and professional relationships, and opportunities also play a role in an actor's career.
The actors with the highest degree centrality are mainly from the adult film industry. Degree centrality measures the number of connections (edges) each node (actor) has in a network (movies), so a high degree centrality suggests that these actors have appeared in a large number of movies and/or have worked with a large number of other actors and are normally very short movies, so there are more movies done than in other genres.
In the case of the adult film industry, it is likely that these actors have a high degree centrality because they have appeared in many movies with different partners. In addition, the adult film industry may have a higher turnover rate than other industries, leading to a larger pool of actors and more frequent collaborations between actors.
It is worth noting that degree centrality alone may not be a comprehensive measure of an actor's success or influence in the movie industry, as there are other factors such as critical acclaim, box office success, and industry connections that can also contribute to an actor's overall impact.
Obtain the list of the 20 actors with the largest betweenness centrality. Show a list with the betweenness, the name of the actor, the number of movies, the main genre, and the number of genres in which the actor has participated.
8.3) Who is the actor with highest betweenes?
Betweenness centrality is a measure of the importance of a node (actor) in a network. It is based on the number of shortest paths between pairs of nodes that pass through that node. In other words, a node with high betweenness centrality is one that lies on many of the shortest paths between other nodes in the network.
df_with_genres.sort_values(by='betweenness', ascending=False).head(1)
| id | ActorName | movies_95_04 | main_genre | Mystery | Adult | Fantasy | Action | Family | Romance | Comedy | Music | Crime | Horror | Animation | Thriller | Musical | NULL | Drama | Western | Adventure | Short | Documentary | War | Sci-Fi | betweenness_weigth | pagerank_weigth | eigenvector_weight | closeness_weight | neighbors | degree | betweenness | closeness | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 10548 | n2108 | Jeremy, Ron | 280 | Adult | 0 | 149 | 0 | 0 | 0 | 3 | 15 | 2 | 0 | 9 | 1 | 3 | 1 | 43 | 15 | 0 | 1 | 4 | 26 | 0 | 8 | 1.487206e+07 | 0.000465 | 0.752032 | 0.836407 | [83, 104, 131, 183, 199, 216, 301, 345, 365, 4... | 471 | 9.748544e+06 | 0.28272 |
Ron Jeremy is a former adult film actor and director who has been active in the industry for over three decades. He has appeared in over 2,000 adult films and is known as one of the most recognizable figures in the industry. The fact that he has worked with 471 other actors and has the highest betweenness centrality suggests that he has played a significant role in connecting different parts of the network. In other words, he is a central figure who has helped to bridge different groups of actors, which has led to him having a high level of influence in the network. This actor seems to be well-connected with other actors in the network, and is frequently on the shortest path between other pairs of actors. This could be due to his longevity in the industry and his ability to form connections with a wide variety of other actors. It is also possible that his high betweenness centrality is due to factors such as his reputation, level of fame, and ability to bring together people from different parts of the industry. Overall, the high betweenness centrality of Ron Jeremy suggests that he has had a significant impact on the adult film industry and has played a key role in connecting different parts of the network, which can be seen in the graph below.
# Get the neighbors of n2108
neighbors = g.neighbors('n2108')
# Create a subgraph with n2108 and its neighbors
subgraph_Ron = g.subgraph(['n2108'] + neighbors)
# Set the ActorName as the vertex label for node 'n2108'
subgraph_Ron.vs.find('n2108')['label'] = nodes.loc[nodes['id'] == 'n2108', 'ActorName'].values[0]
# Plot the subgraph with only the label for node 'n2108'
visual_style = {}
visual_style["vertex_size"] = 20
visual_style["bbox"] = (800, 800)
visual_style["margin"] = 100
visual_style["vertex_label"] = [v['ActorName'] if v.index == subgraph_Ron.vs.find('n2108').index else '' for v in subgraph_Ron.vs]
#ig.plot(subgraph_Ron, **visual_style)
# Get the neighbors of n2108
neighbors = g.neighbors('n2108')
# Count the number of neighbors
num_neighbors = len(neighbors)
# Print the result
print(f"The number of neighbors of n2108 is {num_neighbors}")
The number of neighbors of n2108 is 471
#weights adding
# Get the betweenness centrality of each actor
betweenness = g.betweenness(directed=False)
# Create a dataframe with the actor ids and their betweenness centrality
betweenness_df = pd.DataFrame({'id': g.vs['name'], 'Betweenness': betweenness})
# Merge the betweenness dataframe with the nodes dataframe to get additional information
merged_df = pd.merge(betweenness_df, nodes, on='id')
# Split the genres column into number of genres and add as a new column
merged_df['no_of_genres'] = merged_df['genres'].str.count(',') + 1
# Sort the merged dataframe by betweenness centrality in descending order and get the top 20 actors
top_actors = merged_df.sort_values('Betweenness', ascending=False).head(20).reset_index(drop=True)
# Add a ranking column
top_actors['Rank'] = top_actors.index + 1
# Reorder the columns
top_actors = top_actors[['Rank', 'Betweenness', 'ActorName', 'movies_95_04', 'main_genre', 'no_of_genres']]
# Print the top actors with their betweenness, name, number of movies, main genre, and number of genres participated in
print(top_actors)
Rank Betweenness ActorName movies_95_04 main_genre \
0 1 9.748544e+06 Jeremy, Ron 280 Adult
1 2 4.716909e+06 Chan, Jackie (I) 59 Comedy
2 3 4.330663e+06 Cruz, Penélope 46 Drama
3 4 4.295503e+06 Shahlavi, Darren 16 Action
4 5 4.267099e+06 Del Rosario, Monsour 20 Action
5 6 4.037356e+06 Depardieu, Gérard 56 Comedy
6 7 2.570247e+06 Bachchan, Amitabh 35 Romance
7 8 2.539614e+06 Jackson, Samuel L. 97 Drama
8 9 2.368164e+06 Soualem, Zinedine 65 Comedy
9 10 2.316388e+06 Del Rio, Olivia 84 Adult
10 11 2.136980e+06 Jaenicke, Hannes 66 Thriller
11 12 2.117390e+06 Hayek, Salma 44 Drama
12 13 2.098485e+06 Pelé 10 Romance
13 14 2.062585e+06 Knaup, Herbert 50 Drama
14 15 2.051621e+06 Goldberg, Whoopi 109 Comedy
15 16 2.019247e+06 Roth, Cecilia 23 Drama
16 17 2.006221e+06 Bellucci, Monica 43 Drama
17 18 1.977252e+06 Hanks, Tom 75 Family
18 19 1.937362e+06 August, Pernilla 31 Drama
19 20 1.919261e+06 Kier, Udo 69 Drama
no_of_genres
0 14
1 12
2 13
3 9
4 9
5 11
6 13
7 14
8 12
9 6
10 12
11 14
12 4
13 11
14 14
15 8
16 12
17 13
18 10
19 15
The high betweenness of the top-20 list suggests that these actors are likely to play important roles in connecting different parts of the movie industry network. Actors with high betweenness centrality act as bridges between different groups of actors and movies, and they play a crucial role in the flow of information, resources, and opportunities across the network. As examples, Chackie Chan and Penelope Cruz have also worked with a diverse range of actors and have appeared in many films, leading to their high betweenness centrality scores. On the other hand, Darren Shahlavi was a British actor who primarily worked in action movies. He participated in only 16 movies between 1995 and 2004, but he has a high betweenness centrality because he acted as a bridge between different clusters in the movie network. Many of the movies he acted in were martial arts or action movies, and he worked with many different actors and directors in this genre.
In other words, these actors have the potential to control the flow of information and resources, as they are positioned in the network to serve as intermediaries between otherwise disconnected groups. This makes them valuable assets in the industry, as they can facilitate collaboration, innovation, and success. It is also possible that some of the actors on the list have achieved high betweenness centrality due to their involvement in particular genres or types of movies that are well-connected within the network, making them important figures in those areas.
We also see actors from a variety of genres including Adult, Comedy, Drama, and Thriller, indicating that high betweenness centrality is not limited to actors in any one particular genre and shows the importance of these actors in a field.
df_with_genres.sort_values(by='betweenness_weigth', ascending=False).head(1)
| id | ActorName | movies_95_04 | main_genre | Mystery | Adult | Fantasy | Action | Family | Romance | Comedy | Music | Crime | Horror | Animation | Thriller | Musical | NULL | Drama | Western | Adventure | Short | Documentary | War | Sci-Fi | betweenness_weigth | pagerank_weigth | eigenvector_weight | closeness_weight | neighbors | degree | betweenness | closeness | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 12147 | n162 | Davis, Mark (V) | 540 | Adult | 0 | 429 | 1 | 1 | 0 | 0 | 3 | 0 | 1 | 0 | 0 | 0 | 0 | 92 | 6 | 0 | 0 | 1 | 5 | 0 | 1 | 1.703766e+07 | 0.000427 | 0.980388 | 0.835476 | [83, 86, 104, 131, 137, 139, 142, 181, 184, 29... | 784 | 931853.137495 | 0.2493 |
# Get the betweenness centrality of each actor
betweenness = g_inv.betweenness(weights='weight', directed=False)
# Create a dataframe with the actor ids and their betweenness centrality
betweenness_weight = pd.DataFrame({'id': g_inv.vs['name'], 'Betweenness_w': betweenness})
# Merge the betweenness dataframe with the nodes dataframe to get additional information
merged_df = pd.merge(betweenness_weight, nodes, on='id')
# Split the genres column into number of genres and add as a new column
merged_df['no_of_genres'] = merged_df['genres'].str.count(',') + 1
# Sort the merged dataframe by betweenness centrality in descending order and get the top 20 actors
top_actors = merged_df.sort_values('Betweenness_w', ascending=False).head(20).reset_index(drop=True)
# Add a ranking column
top_actors['Rank'] = top_actors.index + 1
# Reorder the columns
top_actors = top_actors[['Rank', 'Betweenness_w', 'ActorName', 'movies_95_04', 'main_genre', 'no_of_genres']]
# Print the top actors with their betweenness, name, number of movies, main genre, and number of genres participated in
print(top_actors)
Rank Betweenness_w ActorName movies_95_04 main_genre \
0 1 1.703766e+07 Davis, Mark (V) 540 Adult
1 2 1.487206e+07 Jeremy, Ron 280 Adult
2 3 9.651273e+06 Holmes, Steve 288 Adult
3 4 9.200956e+06 Cruz, Penélope 46 Drama
4 5 8.789476e+06 Perry, David (VI) 262 Adult
5 6 7.823371e+06 Blum, Steven (I) 121 Sci-Fi
6 7 7.444954e+06 Cruise, Tom 46 Music
7 8 6.827682e+06 Goldberg, Whoopi 109 Comedy
8 9 6.052977e+06 Depardieu, Gérard 56 Comedy
9 10 6.015747e+06 Norris, Daran 79 Sci-Fi
10 11 5.973748e+06 Welker, Frank 159 Family
11 12 5.217806e+06 Chan, Jackie (I) 59 Comedy
12 13 5.163213e+06 DeLisle, Grey 86 Sci-Fi
13 14 5.149972e+06 Ice-T 63 Drama
14 15 4.797456e+06 Jackson, Samuel L. 97 Drama
15 16 4.673281e+06 Cleese, John 69 Comedy
16 17 4.584522e+06 Schwarzenegger, Arnold 70 Family
17 18 4.374866e+06 Richardson, Kevin Michael 120 Sci-Fi
18 19 4.354148e+06 Lopez, Jennifer (I) 68 Music
19 20 4.285954e+06 Shahlavi, Darren 16 Action
no_of_genres
0 10
1 14
2 5
3 13
4 7
5 14
6 11
7 14
8 11
9 15
10 16
11 12
12 13
13 15
14 14
15 16
16 13
17 17
18 11
19 9
The result of the weighted betweenness analysis suggests that the actors with the highest betweenness centrality are different from the ones with the highest unweighted betweenness. In particular, the weighted analysis takes into account not only the number of shortest paths that pass through an actor but also the weights of the edges. In this case, the weights represent the number of movies that the actors have participated in.
Interestingly, the actor with the highest weighted betweenness is Mark Davis, who has participated in 540 movies in the adult genre and also has the highest degree centrality. This indicates that he is a central actor in the network of actors in the adult movie industry.
Other actors with high weighted betweenness are also from the adult movie industry, such as Ron Jeremy, Steve Holmes, and David Perry. This suggests that these actors have worked with many other actors in the industry, possibly due to the nature of the adult movie genre where actors may have to work with different partners in different movies.
However, there are also actors from other genres with high weighted betweenness, such as Penélope Cruz, Whoopi Goldberg, Gérard Depardieu, and Samuel L. Jackson. This suggests that these actors have participated in many movies and worked with many other actors, making them important bridges between different parts of the network.
Overall, the result of the weighted betweenness analysis provides a more nuanced understanding of the centrality of actors in the network, taking into account the weights of the edges and revealing the importance of actors in the adult movie industry as well as bridges between different actors/genres.
Obtain the list of the 20 actors with the largest closeness centrality. Show a list with the closeness the name of the actor, the number of movies, the main genre, and the number of genres in which the actor has participated.
8.5 Who is the actor with highest closeness centrality?
df_with_genres.sort_values(by='closeness', ascending=False).head(1)
| id | ActorName | movies_95_04 | main_genre | Mystery | Adult | Fantasy | Action | Family | Romance | Comedy | Music | Crime | Horror | Animation | Thriller | Musical | NULL | Drama | Western | Adventure | Short | Documentary | War | Sci-Fi | betweenness_weigth | pagerank_weigth | eigenvector_weight | closeness_weight | neighbors | degree | betweenness | closeness | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 16050 | n17737 | Zhou, Xun | 10 | Drama | 1 | 0 | 0 | 0 | 0 | 3 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 4 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0 | 0.000057 | 0.0 | 2.0 | [12747] | 1 | 0.0 | 1.0 |
# Get the neighbors
neighbors = g.neighbors('n17737')
# Create a subgraph with n17737 and its neighbors
subgraph_Xun = g.subgraph(['n17737'] + neighbors)
# Set the ActorName as the vertex label for node 'n17737'
subgraph_Xun.vs.find('n17737')['label'] = nodes.loc[nodes['id'] == 'n17737', 'ActorName'].values[0]
# Plot the subgraph with only the label for node 'n17737'
visual_style = {}
visual_style["vertex_size"] = 20
visual_style["bbox"] = (300, 300)
visual_style["margin"] = 100
visual_style["vertex_label"] = [v['ActorName'] if v.index == subgraph_Xun.vs.find('n17737').index else '' for v in subgraph_Xun.vs]
#ig.plot(subgraph_Xun, **visual_style)
Closeness is a measure of how easily a node can access other nodes in a network. In a network where there are multiple nodes with the same closeness, selecting only one of them as the "top" node would not be appropriate, as it would not fully capture the overall network structure. In this case, all the nodes in the top 29 have the same closeness degree, indicating that they are all equally central in the network. Therefore, it would be more appropriate to consider all of them as the top nodes rather than just one.
Furthermore, selecting only one node as the top node in a network can be misleading, as it may not fully represent the overall network structure. It is important to consider all nodes with high closeness in order to obtain a more complete picture of the network.
# Filter by subgraph_num = 0
filtered_df = df_with_genres[df_with_genres['subgraph_num'] == 0]
# Sort by closeness in descending order
sorted_df = filtered_df.sort_values(by='closeness', ascending=False)
# Print the top row of the sorted dataframe
top_actors = sorted_df[['closeness', 'id','ActorName', 'movies_95_04', 'main_genre', 'neighbors']]
top_actors.head(1)
| closeness | id | ActorName | movies_95_04 | main_genre | neighbors | |
|---|---|---|---|---|---|---|
| 10412 | 0.309265 | n1529 | Jackson, Samuel L. | 97 | Drama | [1, 8, 48, 67, 80, 100, 116, 161, 168, 177, 25... |
For this measure it could be beneficial to look at Subgraph 1 sinec it represent 99% of all edges and nodes: The result suggests that among all the actors present in subgraph 1, Samuel L. Jackson has the highest closeness centrality, which means he has the shortest path to connect to all other actors in the same subgraph. This indicates that he has worked with many actors within subgraph 1, and therefore, he could be considered as a central and influential actor in this particular subgraph. It is also interesting to note that he has been involved in a large number of movies (97) in the time period between 1995 and 2004, which indicates his prolific acting career during that period.
# Filter the dataframe by subgraph_num = 0
subgraph_df = df_with_genres[df_with_genres['subgraph_num'] == 0]
# Sort the filtered dataframe by closeness in descending order and get the top actor
top_actor = subgraph_df.sort_values(by='closeness', ascending=False).head(1)
# Print the top actor with their Closeness, name, number of movies, main genre, and number of genres participated in
print("Top actor in subgraph 0:\n", top_actor[['closeness', 'ActorName', 'movies_95_04', 'main_genre', 'no_of_genres']])
# Get the neighbors
neighbors = g.neighbors('n1529')
# Create a subgraph with n1529 and its neighbors
subgraph_Xun = g.subgraph(['n1529'] + neighbors)
# Set the ActorName as the vertex label for node 'n1529'
subgraph_Xun.vs.find('n1529')['label'] = nodes.loc[nodes['id'] == 'n1529', 'ActorName'].values[0]
# Plot the subgraph with only the label for node 'n1529'
visual_style = {}
visual_style["vertex_size"] = 20
visual_style["bbox"] = (300, 300)
visual_style["margin"] = 100
visual_style["vertex_label"] = [v['ActorName'] if v.index == subgraph_Xun.vs.find('n1529').index else '' for v in subgraph_Xun.vs]
#ig.plot(subgraph_Xun, **visual_style)
If we consider all subgraphs equally.
# Get the closeness centrality of each actor
closeness = g.closeness(vertices= g.vs,normalized= True, mode= "all")
# Create a dataframe with the actor ids and their closeness centrality
closseness_df = pd.DataFrame({'id': g.vs['name'], 'Closeness': closeness})
# Merge the closeness dataframe with the nodes dataframe to get additional information
merged_df = pd.merge(closseness_df, nodes, on='id')
# Split the genres column into number of genres and add as a new column
merged_df['no_of_genres'] = merged_df['genres'].str.count(',') + 1
# Sort the merged dataframe by closseness centrality in descending order and get the top 20 actors
top_actors = merged_df.sort_values('Closeness', ascending=False).head(29).reset_index(drop=True)
# Add a ranking column
top_actors['Rank'] = top_actors.index + 1
# Reorder the columns
top_actors = top_actors[['Rank', 'Closeness', 'ActorName', 'movies_95_04', 'main_genre', 'no_of_genres']]
# Print the top actors with their Closeness, name, number of movies, main genre, and number of genres participated in
print(top_actors)
Rank Closeness ActorName movies_95_04 main_genre no_of_genres 0 1 1.0 Zhou, Xun 10 Drama 5 1 2 1.0 Servais, Manuela 11 Drama 4 2 3 1.0 Shakibai, Khosro 12 Drama 3 3 4 1.0 Kuusniemi, Matti 10 Horror 6 4 5 1.0 Nawrocki, Mike 20 Family 4 5 6 1.0 Holt, David (III) 13 Comedy 6 6 7 1.0 Chamish, Leanna 11 Horror 7 7 8 1.0 Lawson, Denis 13 Drama 8 8 9 1.0 Vischer, Lisa 16 Family 4 9 10 1.0 Walsh, Darren (I) 18 Comedy 4 10 11 1.0 Wood, T.J. 14 Adult 2 11 12 1.0 Wang, Zhiwen 10 Drama 4 12 13 1.0 Doyle, John (I) 11 Comedy 2 13 14 1.0 Sterne, Jeff (II) 20 Adult 2 14 15 1.0 Mizuki, Arisa 11 Drama 4 15 16 1.0 Poole, Jim 10 Animation 4 16 17 1.0 Neo, Jack 11 Comedy 6 17 18 1.0 Vischer, Phil 20 Family 4 18 19 1.0 Pickhaver, Greig 11 Comedy 2 19 20 1.0 Matsushita, Yuki 11 Drama 4 20 21 1.0 Tork, Hanan 13 Romance 4 21 22 1.0 Stover, George 11 Sci-Fi 6 22 23 1.0 Nor, Rosyam 12 Romance 4 23 24 1.0 Zaki, Mona 16 Romance 6 24 25 1.0 Kianian, Reza 11 Thriller 4 25 26 1.0 Lähde, Ville (I) 10 Horror 6 26 27 1.0 Black, Rick (II) 11 Adult 2 27 28 1.0 Fox, Emilia 18 Drama 9 28 29 1.0 De Neck, Didier 11 Drama 6
The actors listed in the DataFrame have a closeness score of 1.0, which means they are the most central nodes in their respective subgraphs. However, it's important to note that most of these subgraphs have only two nodes, which indicates that these actors have acted in movies with only one other actor in the dataset.
It's difficult to draw any conclusions about the actors' backgrounds based solely on this information. However, we can note that there are actors from a variety of genres represented in the list, including drama, horror, comedy, family, romance, sci-fi, and thriller. Some of the actors are relatively unknown, while others, such as Emilia Fox, have had successful acting careers.
It's possible that the isolation of these actors is due to a number of factors, such as the regional or cultural focus of the movies they've acted in, or the fact that they may be more likely to act in independent or art house films with smaller casts. For that reason using the closeness for subgraph 1 is more appropiate to get an actor with many close relationships.
# Find the largest connected component
largest_component = g.clusters().giant()
# Get the closeness centrality of each actor in the largest connected component
closeness = largest_component.closeness(vertices=largest_component.vs, normalized=True)
# Create a dataframe with the actor ids and their closeness centrality
closeness_df = pd.DataFrame({'id': largest_component.vs['name'], 'Closeness': closeness})
# Merge the closeness dataframe with the nodes dataframe to get additional information
merged_df = pd.merge(closeness_df, nodes, on='id')
# Split the genres column into number of genres and add as a new column
merged_df['no_of_genres'] = merged_df['genres'].str.count(',') + 1
# Sort the merged dataframe by closeness centrality in descending order and get the top 20 actors
top_actors = merged_df.sort_values('Closeness', ascending=False).head(20).reset_index(drop=True)
# Add a ranking column
top_actors['Rank'] = top_actors.index + 1
# Reorder the columns
top_actors = top_actors[['Rank', 'Closeness', 'ActorName', 'movies_95_04', 'main_genre', 'no_of_genres']]
# Print the top actors with their Closeness, name, number of movies, main genre, and number of genres participated in
print(top_actors)
<ipython-input-56-c31a5f752cce>:2: DeprecationWarning: Graph.clusters() is deprecated; use Graph.connected_components() instead
Rank Closeness ActorName movies_95_04 main_genre \
0 1 0.309265 Jackson, Samuel L. 97 Drama
1 2 0.307760 Goldberg, Whoopi 109 Comedy
2 3 0.305905 Berry, Halle 63 Family
3 4 0.305669 Diaz, Cameron 59 Drama
4 5 0.305231 Hanks, Tom 75 Family
5 6 0.304719 Stiller, Ben 66 Comedy
6 7 0.302611 Myers, Mike (I) 58 Comedy
7 8 0.302606 Douglas, Michael (I) 41 Family
8 9 0.301217 Lopez, Jennifer (I) 68 Music
9 10 0.300708 De Niro, Robert 51 Comedy
10 11 0.300485 Willis, Bruce (I) 52 Thriller
11 12 0.300408 Cruise, Tom 46 Music
12 13 0.299336 Hopper, Dennis 106 Music
13 14 0.298768 Kidman, Nicole 54 Family
14 15 0.298553 Smith, Will (I) 57 Music
15 16 0.298548 Washington, Denzel 49 Family
16 17 0.298512 Travolta, John 63 Drama
17 18 0.298359 Madonna (I) 61 Music
18 19 0.297743 Schwarzenegger, Arnold 70 Family
19 20 0.297581 Hoffman, Dustin 56 Thriller
no_of_genres
0 14
1 14
2 14
3 13
4 13
5 14
6 10
7 11
8 11
9 13
10 14
11 11
12 16
13 12
14 10
15 10
16 13
17 11
18 13
19 12
Subgraph 1 From the result, we can see that the top 5 actors with the highest closeness centrality are Jackson, Samuel L., Goldberg, Whoopi, Berry, Halle, Diaz, Cameron, and Hanks, Tom. This suggests that these actors are important in connecting other actors in the subgraph and are likely to have acted in many movies together with other actors in the subgraph.
For example, Samuel L. Jackson has a closeness of 0.309, which means he is only a few steps away from most other actors in the network. Jackson has appeared in many movies with other actors on this list, such as Bruce Willis, Whoopi Goldberg, and Robert De Niro, who themselves have also worked with many other actors in the network.
Furthermore, we can see that the main genres of the top actors with high closeness centrality are mostly in Drama, Comedy, Family, and Thriller. This indicates that these genres may be popular within the subgraph and that these actors may have played significant roles in movies of these genres.
Lastly, we can also see that the number of genres for each actor varies from 16 to 10. This suggests that the actors with higher numbers of genres have acted in movies of diverse genres, which may have contributed to their high closeness centrality.
Weighted closeness can be more appropriate than unweighted closeness in certain scenarios. For example, if we are modeling a social network where some connections are stronger (e.g. close friendships) than others (e.g. casual acquaintances), weighted closeness can give us a better sense of who is most central in the network
# Filter the dataframe by subgraph_num = 0
subgraph_df = df_with_genres[df_with_genres['subgraph_num'] == 0]
# Sort the filtered dataframe by closeness in descending order and get the top actor
top_actor = subgraph_df.sort_values(by='closeness_weight', ascending=False).head(1)
# Print the top actor with their Closeness, name, number of movies, main genre, and number of genres participated in
print("Top actor in subgraph 0:\n", top_actor[['closeness_weight', 'ActorName', 'movies_95_04', 'main_genre']])
Top actor in subgraph 0:
closeness_weight ActorName movies_95_04 main_genre
10412 0.946299 Jackson, Samuel L. 97 Drama
# Filter the dataframe by subgraph_num = 0
subgraph_df = df_with_genres[df_with_genres['subgraph_num'] == 0]
# Sort the filtered dataframe by closeness in descending order and get the top actor
top_actor = subgraph_df.sort_values(by='closeness_weight', ascending=False).head(20)
top_actor = top_actor[['id', 'ActorName','movies_95_04','main_genre','closeness_weight','neighbors']]
top_actor.head(20)
| id | ActorName | movies_95_04 | main_genre | closeness_weight | neighbors | |
|---|---|---|---|---|---|---|
| 10412 | n1529 | Jackson, Samuel L. | 97 | Drama | 0.946299 | [1, 8, 48, 67, 80, 100, 116, 161, 168, 177, 25... |
| 9624 | n1459 | Cruise, Tom | 46 | Music | 0.935858 | [8, 12, 48, 67, 168, 256, 269, 456, 604, 617, ... |
| 8119 | n3268 | Berry, Halle | 63 | Family | 0.933481 | [67, 168, 173, 231, 256, 269, 272, 388, 411, 4... |
| 4524 | n701 | Hanks, Tom | 75 | Family | 0.932165 | [8, 65, 67, 168, 177, 211, 231, 249, 256, 306,... |
| 1090 | n689 | Schwarzenegger, Arnold | 70 | Family | 0.930866 | [12, 33, 168, 177, 216, 251, 256, 269, 291, 30... |
| 1072 | n4427 | Spielberg, Steven | 99 | Family | 0.927778 | [8, 48, 67, 116, 161, 168, 177, 256, 293, 334,... |
| 3542 | n2674 | Lopez, Jennifer (I) | 68 | Music | 0.925982 | [42, 62, 67, 72, 116, 123, 168, 174, 204, 211,... |
| 5832 | n3142 | Ford, Harrison (I) | 51 | Thriller | 0.925373 | [48, 80, 97, 116, 161, 168, 177, 256, 311, 456... |
| 9655 | n3213 | Goldberg, Whoopi | 109 | Comedy | 0.924991 | [8, 48, 67, 116, 211, 245, 256, 297, 343, 473,... |
| 13804 | n558 | Stiller, Ben | 66 | Comedy | 0.924385 | [42, 67, 161, 168, 256, 308, 330, 343, 440, 45... |
| 3588 | n503 | Diaz, Cameron | 59 | Drama | 0.924102 | [48, 67, 123, 168, 216, 256, 272, 306, 308, 33... |
| 10169 | n3235 | Travolta, John | 63 | Drama | 0.923801 | [8, 67, 80, 168, 216, 256, 272, 349, 473, 530,... |
| 14799 | n546 | Williams, Robin (I) | 72 | Comedy | 0.922333 | [17, 48, 67, 123, 161, 211, 256, 258, 272, 291... |
| 8534 | n574 | Kidman, Nicole | 54 | Family | 0.918881 | [67, 89, 161, 168, 256, 272, 348, 696, 708, 72... |
| 708 | n3907 | Myers, Mike (I) | 58 | Comedy | 0.917589 | [48, 67, 211, 216, 256, 330, 388, 544, 604, 77... |
| 16433 | n2724 | Welker, Frank | 159 | Family | 0.916674 | [127, 158, 283, 334, 335, 360, 454, 478, 501, ... |
| 1912 | n465 | Nicholson, Jack | 35 | Drama | 0.915593 | [8, 67, 116, 123, 211, 256, 272, 544, 582, 617... |
| 2198 | n2721 | Bennett, Jeff (I) | 128 | Family | 0.913078 | [127, 278, 318, 335, 360, 454, 567, 584, 663, ... |
| 4905 | n697 | Sandler, Adam (I) | 54 | Comedy | 0.910286 | [161, 306, 330, 388, 544, 708, 732, 825, 869, ... |
| 15694 | n733 | Rock, Chris (I) | 62 | Comedy | 0.910016 | [33, 48, 216, 256, 297, 306, 308, 330, 334, 34... |
The difference is the ranking order, which might have favored certain actors that have more closer connections than others. Therefore, it appears that the weighted version is giving more importance to the network structure (e.g. how actors are connected) than the unweighted version.
In summary, the conclusion that can be drawn is that the weighted version of closeness centrality is taking into account the network structure while the unweighted version is not.
# Find communities using the Louvain method
communities = largest_subgraph.community_multilevel()
# Set the color of each vertex based on the community it belongs to
palette = ig.ClusterColoringPalette(len(communities))
vertex_colors = [palette.get(i) for i in communities.membership]
# Alternatively, you can also set the size and layout of the plot
layout = largest_subgraph.layout('fr')
ig.plot(largest_subgraph, '/content/drive/MyDrive/communities_subgraph.png', vertex_color=vertex_colors, layout=layout, bbox=(800, 800))
Community structure based on the multilevel algorithm of Blondel et al.
This is a bottom-up algorithm: initially every vertex belongs to a separate community, and vertices are moved between communities iteratively in a way that maximizes the vertices' local contribution to the overall modularity score. When a consensus is reached (i.e. no single move would increase the modularity score), every community in the original graph is shrank to a single vertex (while keeping the total weight of the adjacent edges) and the process continues on the next level. The algorithm stops when it is not possible to increase the modularity any more after shrinking the communities to vertices.
The identification of communities provide valuable insights into the underlying structure and organization of the actor's network. The communities reveal groups of nodes that are more closely related to each other. Communities can also reveal patterns of influence or information flow within a network, where nodes within a community may be more likely to influence or receive information from other nodes within the same community.